{"id":12190,"date":"2021-10-06T00:00:39","date_gmt":"2021-10-06T07:00:39","guid":{"rendered":"https:\/\/www.couchbase.com\/blog\/?p=12190"},"modified":"2025-06-13T21:25:12","modified_gmt":"2025-06-14T04:25:12","slug":"linear-regression-model-couchbase-jupyter-notebook-tutorial","status":"publish","type":"post","link":"https:\/\/www.couchbase.com\/blog\/ko\/linear-regression-model-couchbase-jupyter-notebook-tutorial\/","title":{"rendered":"\uce74\uc6b0\uce58\ubca0\uc774\uc2a4\uc640 \uc8fc\ud53c\ud130 \ub178\ud2b8\ubd81\uc73c\ub85c \uc120\ud615 \ud68c\uadc0 \ubaa8\ub378 \ud6c8\ub828\ud558\uae30: \ub2e8\uacc4\ubcc4 \ud29c\ud1a0\ub9ac\uc5bc"},"content":{"rendered":"

\ub370\uc774\ud130 \uacfc\ud559\uc790\ub4e4\uc774 \uc0ac\ub791\ud558\ub294 Jupyter \ub178\ud2b8\ubd81<\/strong> - \ub97c \uc0ac\uc6a9\ud558\uba74 Couchbase \ubb38\uc11c \ub370\uc774\ud130\ubca0\uc774\uc2a4\uc640 \uc790\uc5f0\uc2a4\ub7fd\uac8c \ud398\uc5b4\ub9c1\ub429\ub2c8\ub2e4. <\/p>\n

\"Jupyter<\/p>\n

\uc65c \uadf8\ub7f4\uae4c\uc694? \uc65c \uc8fc\ud53c\ud130 \ub178\ud2b8\ubd81<\/a> \uc6f9 \uc560\ud50c\ub9ac\ucf00\uc774\uc158\uc744 \uc0ac\uc6a9\ud558\uba74 \ub370\uc774\ud130 \uc2dc\uac01\ud654 \ubc0f \uba38\uc2e0 \ub7ec\ub2dd\uacfc \uac19\uc740 \uc0ac\uc6a9 \uc0ac\ub840\ub97c \uc704\ud574 \uc11c\uc220\ud615 \ud14d\uc2a4\ud2b8, \uc218\uc2dd \ub4f1\uc774 \ud3ec\ud568\ub41c \ubb38\uc11c\ub97c \ub9cc\ub4e4\uace0 \uacf5\uc720\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uce74\uc6b0\uce58\ubca0\uc774\uc2a4<\/a> \ub97c \uc0ac\uc6a9\ud558\uba74 \ubc29\ub300\ud55c \uc591\uc758 \ub370\uc774\ud130(\ubc18\uc815\ud615 \ubc0f \ube44\uc815\ud615)\ub97c \ub300\uaddc\ubaa8\ub85c \uc800\uc7a5\ud558\uace0 \ucc98\ub9ac\ud560 \uc218 \uc788\uc73c\uba70, \ub0b4\ub7ec\ud2f0\ube0c \ud14d\uc2a4\ud2b8(\uc18c\uc15c \ubbf8\ub514\uc5b4 \uac8c\uc2dc\ubb3c \ub4f1), \ubc29\uc815\uc2dd \ub4f1 \uc138\uc0c1\uc5d0 \ub118\uccd0\ub098\ub294 \ubaa8\ub4e0 \uc885\ub958\uc758 \ub370\uc774\ud130\ub97c \uc9c0\uc6d0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. <\/p>\n

\uc774 \uae00\uc5d0\uc11c\ub294 Couchbase \ud074\ub7ec\uc2a4\ud130\uc640 Jupyter Notebook \uac04\uc758 \uc5f0\uacb0\uc744 \uc124\uc815\ud55c \ub2e4\uc74c, Couchbase\uc5d0\uc11c \ub370\uc774\ud130\ub97c \uac00\uc838\uc640\uc11c \uba38\uc2e0 \ub7ec\ub2dd\uc744 \uc704\ud55c \uc120\ud615 \ud68c\uadc0 \ubaa8\ub378\uc744 \ud6c8\ub828\ud558\ub294 \ubc29\ubc95\uc744 \uc54c\uc544\ubd05\ub2c8\ub2e4. \uc120\ud615 \ud68c\uadc0 \ubc29\uc815\uc2dd\uc744 \ud1b5\ud574 \ubc94\uc8fc\ud615 \ubcc0\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \ub300\uc0c1 \ubcc0\uc218\uc758 \uac12\uc744 \uc608\uce21\ud558\ub294 \uc608\uc81c\ub97c \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. <\/p>\n

\ub370\uc774\ud130 \ub85c\ub4dc<\/h2>\n

\uc2dc\uc791\ud558\ub824\uba74 \ub2e4\uc74c \ub2e8\uacc4\ub97c \uc9c4\ud589\ud558\uc5ec \uc0d8\ud50c \ub370\uc774\ud130 \uc138\ud2b8\ub97c \ub85c\ub4dc\ud569\ub2c8\ub2e4: <\/p>\n

    \n
  1. Couchbase \ud074\ub7ec\uc2a4\ud130\uc758 \uad00\ub9ac\uc790 \ucf58\uc194\uc5d0\uc11c \ub2e4\uc74c\uacfc \uac19\uc774 \ubc84\ud0b7 > \ubc84\ud0b7 \ucd94\uac00\ub85c \uc774\ub3d9\ud558\uc5ec \uc0c8 \ubc84\ud0b7\uc744 \ub9cc\ub4ed\ub2c8\ub2e4:
    \n
    \n    \"Add<\/a>\n<\/li>\n
  2. \n

    \ub2e4\uc74c\uacfc \uac19\uc774 \ubb38\uc11c > \ubb38\uc11c \ucd94\uac00\ub85c \uc774\ub3d9\ud558\uc5ec \ubc84\ud0b7\uc5d0 \ubb38\uc11c\ub97c \ucd94\uac00\ud569\ub2c8\ub2e4:<\/p>\n

    \n\"Add<\/a><\/p>\n

    \ub97c \uc0ac\uc6a9\ud558\uac70\ub098 JSON \ubb38\uc11c \ubaa9\ub85d \ub610\ub294 CSV \ud30c\uc77c\uc744 \uc5c5\ub85c\ub4dc\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \uc608\uc81c\uc5d0\uc11c\ub294 \ub2e4\uc74c\uc744 \uc0ac\uc6a9\ud558\uc5ec CSV \ud30c\uc77c\uc744 \uc5c5\ub85c\ub4dc\ud558\uaca0\uc2b5\ub2c8\ub2e4. `cbimport`<\/a>. \ub0b4 \ubb38\uc11c\uc758 \ubaa8\uc2b5\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:<\/p>\n

    \n\"CSV<\/a><\/p>\n<\/li>\n

  3. \ud30c\uc77c\uc740 \uc791\uc5c5\ud558\ub824\ub294 \ubaa8\ub4e0 \ub370\uc774\ud130\uac00 \ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \uc608\uc81c\uc5d0\uc11c\ub294 Kaggle\uc758 \uad11\uace0 \ub370\uc774\ud130 \uc138\ud2b8.<\/a><\/li>\n
  4. \uadf8\ub9bc\uacfc \uac19\uc774 \ubb38\uc11c > \uac00\uc838\uc624\uae30\ub85c \uc774\ub3d9\ud569\ub2c8\ub2e4:
    \n
    \n    \"Import<\/a><\/p>\n<\/li>\n
  5. \n

    \uac00\uc838\uc624\ub824\ub294 \ud30c\uc77c\uacfc \ubb38\uc11c\uac00 \uc788\ub294 \ub370\uc774\ud130 \ubc84\ud0b7\uc744 \uc120\ud0dd\ud569\ub2c8\ub2e4:<\/p>\n

    \n\"Select<\/a><\/p>\n

    \uc774\uc81c \ubb38\uc11c \uba54\ub274\uac00 \ub2e4\uc74c\uacfc \uac19\uc774 \ud45c\uc2dc\ub429\ub2c8\ub2e4:<\/p>\n

    \n\"The<\/a><\/p>\n<\/li>\n

  6. \uad00\ub9ac \ucf58\uc194\uc5d0\uc11c \ub2e4\uc74c\uacfc \uac19\uc774 \ub370\uc774\ud130 \ubc84\ud0b7\uc5d0 \ub300\ud55c \uae30\ubcf8 \uc778\ub371\uc2a4\ub97c \ub9cc\ub4e4\uc5b4 \ub370\uc774\ud130\ub97c \ucffc\ub9ac\ud560 \uc218 \uc788\ub3c4\ub85d \ud569\ub2c8\ub2e4:
    \n
    \n    \"Add<\/a>\n<\/li>\n<\/ol>\n
    <\/div>\n

    Jupyter \ub178\ud2b8\ubd81 \uc124\uce58\ud558\uae30<\/h2>\n

    \uba3c\uc800 \uce74\uc6b0\uce58\ubca0\uc774\uc2a4-\uc8fc\ud53c\ud130-\uc608\uc81c \ub2e4\uc6b4\ub85c\ub4dc<\/a> \ub97c \ub2e4\uc6b4\ub85c\ub4dc\ud569\ub2c8\ub2e4. \uadf8\ub7f0 \ub2e4\uc74c \ub2e4\uc74c \ub2e8\uacc4\ub97c \ub530\ub974\uc138\uc694:<\/p>\n

      \n
    1. \uc8fc\ud53c\ud130 \ub178\ud2b8\ubd81 \uc124\uce58<\/a> via either the Python package management system (`pip`) or \uc544\ub098\ucf58\ub2e4.<\/a><\/li>\n
    2. Install the dependencies for this project by using `pip` from the `requirements` file in your shell:\n
      \r\n$ pip install -r requirements.txt\r\n<\/pre>\n<\/li>\n
    3. \uc178\uc5d0\uc11c \uc8fc\ud53c\ud130 \ub178\ud2b8\ubd81\uc744 \uc5fd\ub2c8\ub2e4.<\/li>\n
    4. \ub2e4\uc74c\uacfc \uac19\uc774 Python 3\uc73c\ub85c \uc0c8 \ub178\ud2b8\ubd81\uc744 \ub9cc\ub4ed\ub2c8\ub2e4:
      \n
      \n      \"Making<\/a>\n<\/li>\n<\/ol>\n
       <\/div>\n
      \uc120\ud615 \ud68c\uadc0 \ubaa8\ub378\uc774\ub780 \ubb34\uc5c7\uc778\uac00\uc694?<\/h2>\n
      \uc120\ud615 \ud68c\uadc0 \ubaa8\ub378\uc740 \ubc94\uc8fc\ud615 \ub610\ub294 \ub3c5\ub9bd \ubcc0\uc218\uc758 \uac15\ub3c4\ub97c \uacb0\uc815\ud558\uace0 \ud574\ub2f9 \ubcc0\uc218\uc758 \ud6a8\uacfc\ub97c \uc608\uce21\ud558\uace0 \ub370\uc774\ud130\uc758 \ucd94\uc138\ub97c \ud30c\uc545\ud560 \uc218 \uc788\ub294 \uac15\ub825\ud55c \uc608\uce21 \ubd84\uc11d\uc6a9 \ubaa8\ub378\uc785\ub2c8\ub2e4. <\/p>\n
      \uc774\ub984\uc5d0\uc11c \uc720\ucd94\ud560 \uc218 \uc788\ub4ef\uc774 \uc120\ud615 \ud68c\uadc0<\/em>, the \u201ccurve\u201d we use to fit the data is a line. The simplest form of the regression equation is `y = mx + c`, where `y` represents the target variable, `x` represents a single categorical variable and `m` and `c` are constants. We will use a simple linear regression equation in our example.<\/p>\n
      The categorical variables in our example are `TV`, `Radio` and `Newspaper`. The target variable is `Sales`.<\/p>\n
      \uc120\ud615 \ud68c\uadc0 \ubaa8\ub378 \ud6c8\ub828<\/h2>\n
        \n
      1. In the new Jupyter Notebook, use the code shown below to connect to the Couchbase server. Use your username and password, of course, instead of `Administrator` and `123456`.
        \n
        \n        \"Connecting<\/a><\/p>\n<\/li>\n
      2. Import the required libraries, shown in the screenshot here. If these libraries aren\u2019t present in your environment, download the latest versions of these libraries to the right environment using the Python package manager, `pip`.
        \n
        \n        \"Import<\/a><\/p>\n<\/li>\n
      3. Using the `SELECT` command, fetch the data from your data bucket into a pandas data  frame:
        \n
        \n        \"Fetch<\/a><\/p>\n<\/li>\n
      4. You can view the contents of your pandas data frame using the `describe()` command, as shown here:
        \n
        \n        \"Using<\/a><\/p>\n<\/li>\n
      5. \uac01 \ubc94\uc8fc\ud615 \ubcc0\uc218\uc758 \uac12\uc5d0 \ud574\ub2f9\ud558\ub294 \ubc15\uc2a4 \ud50c\ub86f\uc744 \ub9cc\ub4e4\uc5b4 \uc774\uc0c1\uac12\uc744 \uac10\uc9c0\ud569\ub2c8\ub2e4:
        \n
        \n        \"Detect<\/a><\/p>\n<\/li>\n
      6. \uac01 \ubc94\uc8fc\ud615 \ubcc0\uc218\uc5d0 \ub300\ud574 \ub300\uc0c1 \ubcc0\uc218\uc5d0 \ub300\ud55c \ubd84\uc0b0\ud615 \ucc28\ud2b8\ub97c \ub9cc\ub4e4\uc5b4 \uc0c1\uad00 \uad00\uacc4\ub97c \ud30c\uc545\ud569\ub2c8\ub2e4.
        \nNotice that `TV` appears to have the highest degree of correlation.
        \n
        \n        \"Categorical<\/a><\/p>\n<\/li>\n
      7. Split the dataset, using 60% of it for training and the remaining 40% for testing. We can now determine the value of coefficients in the regression equation, when the categorical variable is `TV` and the target variable is `Sales`, using the \uc77c\ubc18 \ucd5c\uc18c \uc81c\uacf1 \ubc29\ubc95<\/a>.
        \n
        \n        \"Ordinary<\/a><\/p>\n<\/li>\n
      8. \uc774\uc81c \uc5ec\uae30\uc5d0 \ud45c\uc2dc\ub41c \ucf54\ub4dc\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub378\uc744 \ud559\uc2b5\uc2dc\ud0b5\ub2c8\ub2e4:
        \n
        \n        \"Training<\/a><\/p>\n<\/li>\n
      9. Next, substitute `test` for `train` to use the model to predict the test set\u2019s values, like this:
        \n
        \n        \"Testing<\/a><\/p>\n<\/li>\n<\/ol>\n
         <\/div>\n
        Using the same approach, we can train and test a model with the categorical variables `Radio` and `Newspaper`, as well:
        \n
        \n        \"Linear<\/a>
        \n        \"Linear<\/a>
        \n        \"Training<\/a>
        \n        \"Testing<\/a><\/p>\n
        \uba38\uc2e0 \ub7ec\ub2dd \ubc0f \uce74\uc6b0\uce58\ubca0\uc774\uc2a4\ub85c \ub354 \ub098\uc544\uac00\uae30<\/h2>\n
        \uc774\uc81c \uc5f0\uacb0\uc5d0 \uc775\uc219\ud574\uc84c\uc73c\ub2c8 \uc774\uc81c \uce74\uc6b0\uce58\ubca0\uc774\uc2a4 \uc11c\ubc84<\/a> \ub85c \uc774\ub3d9\ud558\uc5ec \uc120\ud615 \ud68c\uadc0\uc758 \uba38\uc2e0 \ub7ec\ub2dd \uac1c\ub150\uc744 \uc0b4\ud3b4\ubcf4\uc168\ub2e4\uba74, \ub2e4\uc74c \ud3ec\uc2a4\ud305\uc744 \ud1b5\ud574 \uadf8 \uc9c0\uc2dd\uc744 \uc313\uc73c\uc138\uc694. \uba38\uc2e0 \ub7ec\ub2dd \ubaa8\ub378 \uc800\uc7a5\uc18c\ub85c Couchbase\ub97c \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95<\/a> \uadf8\ub9ac\uace0 Couchbase\ub97c \uc0ac\uc6a9\ud558\uc5ec AI \uae30\ubc18 \uc778\uc0ac\uc774\ud2b8 \ud655\ubcf4\ud558\uae30<\/a>.<\/p>\n
         <\/div>\n
        \uc9c1\uc811 \uc0ac\uc6a9\ud574 \ubcf4\uc138\uc694:

        \n        \uc5ec\uae30\uc5d0\uc11c Couchbase 7 \ub2e4\uc6b4\ub85c\ub4dc<\/a><\/strong><\/div>\n
         <\/div>\n
         
        \n 
        \n <\/p>","protected":false},"excerpt":{"rendered":"
        Data scientists love Jupyter Notebooks \u2013 and it makes a natural pairing with the Couchbase document database. Why? The Jupyter Notebook web application lets you create and share documents that contain narrative text, equations and the like for use cases […]<\/p>","protected":false},"author":79062,"featured_media":12191,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[10122,1815,2381,1816,9139],"tags":[2250,2312,2140],"ppma_author":[9457],"class_list":["post-12190","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-artificial-intelligence-ai","category-best-practices-and-tutorials","category-community","category-couchbase-server","category-python","tag-data-visualization","tag-document-database","tag-machine-learning"],"acf":[],"yoast_head":"\nTraining a Linear Regression Model with Couchbase & a Jupyter Notebook: Step-by-Step Tutorial<\/title>\n<meta name=\"description\" content=\"Follow this step-by-step tutorial on training and testing a machine learning model for deep data insights using Jupyter Notebook and Couchbase Server.\" \/>\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.couchbase.com\/blog\/ko\/linear-regression-model-couchbase-jupyter-notebook-tutorial\/\" \/>\n<meta property=\"og:locale\" content=\"ko_KR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Training a Linear Regression Model with Couchbase & a Jupyter Notebook: Step-by-Step Tutorial\" \/>\n<meta property=\"og:description\" content=\"Follow this step-by-step tutorial on training and testing a machine learning model for deep data insights using Jupyter Notebook and Couchbase Server.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.couchbase.com\/blog\/ko\/linear-regression-model-couchbase-jupyter-notebook-tutorial\/\" \/>\n<meta property=\"og:site_name\" content=\"The Couchbase Blog\" \/>\n<meta property=\"article:published_time\" content=\"2021-10-06T07:00:39+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2025-06-14T04:25:12+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2021\/10\/jupyter-notebook-couchbase-linear-regression-model-tutorial-social.png\" \/>\n\t<meta property=\"og:image:width\" content=\"800\" \/>\n\t<meta property=\"og:image:height\" content=\"418\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"Agrima Khanna\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:image\" content=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2021\/10\/jupyter-notebook-couchbase-linear-regression-model-tutorial-social.png\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Agrima Khanna\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"4\ubd84\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"TechArticle\",\"@id\":\"https:\/\/www.couchbase.com\/blog\/linear-regression-model-couchbase-jupyter-notebook-tutorial\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/www.couchbase.com\/blog\/linear-regression-model-couchbase-jupyter-notebook-tutorial\/\"},\"author\":{\"name\":\"Agrima Khanna\",\"@id\":\"https:\/\/www.couchbase.com\/blog\/#\/schema\/person\/70424341a4f4d4236f120d7ea0488f00\"},\"headline\":\"Training a Linear Regression Model with Couchbase & a Jupyter Notebook: Step-by-Step 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