{"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\/pt\/linear-regression-model-couchbase-jupyter-notebook-tutorial\/","title":{"rendered":"Treinamento de um modelo de regress\u00e3o linear com o Couchbase e um Jupyter Notebook: Tutorial passo a passo"},"content":{"rendered":"

Os cientistas de dados adoram os notebooks Jupyter<\/strong> - e faz uma combina\u00e7\u00e3o natural com o banco de dados de documentos do Couchbase. <\/p>\n

\"Jupyter<\/p>\n

Por qu\u00ea? A Bloco de notas Jupyter<\/a> O aplicativo da Web permite criar e compartilhar documentos que cont\u00eam texto narrativo, equa\u00e7\u00f5es e similares para casos de uso como visualiza\u00e7\u00e3o de dados e aprendizado de m\u00e1quina. Couchbase<\/a> permite que voc\u00ea armazene e processe grandes quantidades de dados (semiestruturados e n\u00e3o estruturados) em escala e ofere\u00e7a suporte aos tipos de dados dos quais o mundo est\u00e1 cheio: texto narrativo (publica\u00e7\u00f5es em m\u00eddias sociais etc.), equa\u00e7\u00f5es e muito mais. <\/p>\n

Nesta publica\u00e7\u00e3o, voc\u00ea aprender\u00e1 a estabelecer a conectividade entre um cluster do Couchbase e um Jupyter Notebook e, em seguida, extrair dados do Couchbase e us\u00e1-los para treinar um modelo de regress\u00e3o linear para aprendizado de m\u00e1quina. Examinaremos um exemplo para prever o valor de uma vari\u00e1vel-alvo usando vari\u00e1veis categ\u00f3ricas por meio de uma equa\u00e7\u00e3o de regress\u00e3o linear. <\/p>\n

Carregando seus dados<\/h2>\n

Para come\u00e7ar, siga estas etapas para carregar o conjunto de dados de amostra: <\/p>\n

    \n
  1. No Admin Console do cluster do Couchbase, v\u00e1 para Buckets > Add Bucket para criar um novo bucket, conforme mostrado aqui:
    \n
    \n    \"Add<\/a>\n<\/li>\n
  2. \n

    Adicione documentos ao seu compartimento navegando at\u00e9 Documents > Add Document (Documentos > Adicionar documento), assim:<\/p>\n

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

    ou fazer upload de uma lista de documentos JSON ou de um arquivo CSV. Para este exemplo, faremos upload de um arquivo CSV usando `cbimport`<\/a>. Este \u00e9 o aspecto do meu documento:<\/p>\n

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

  3. O arquivo pode ser qualquer dado com o qual voc\u00ea queira trabalhar. Este exemplo usa o arquivo Conjunto de dados de publicidade do Kaggle.<\/a><\/li>\n
  4. V\u00e1 para Documents > Import, conforme mostrado aqui:
    \n
    \n    \"Import<\/a><\/p>\n<\/li>\n
  5. \n

    Selecione o arquivo que deseja importar e o intervalo de dados onde os documentos residem:<\/p>\n

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

    Seu menu Documentos agora deve ter a seguinte apar\u00eancia:<\/p>\n

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

  6. No Admin Console, crie um \u00edndice prim\u00e1rio para o bucket de dados para tornar os dados consult\u00e1veis, como voc\u00ea v\u00ea aqui:
    \n
    \n    \"Add<\/a>\n<\/li>\n<\/ol>\n
    <\/div>\n

    Instalando o Jupyter Notebook<\/h2>\n

    Em primeiro lugar, Fa\u00e7a o download do exemplo do couchbase-jupyter<\/a> do reposit\u00f3rio do GitHub do Couchbase Labs. Em seguida, siga estas etapas:<\/p>\n

      \n
    1. Instalar o Jupyter Notebook<\/a> via either the Python package management system (`pip`) or Anaconda.<\/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. Abra o Jupyter Notebook no shell.<\/li>\n
    4. Crie um novo notebook com Python 3, como mostrado aqui:
      \n
      \n      \"Making<\/a>\n<\/li>\n<\/ol>\n
       <\/div>\n
      O que \u00e9 um modelo de regress\u00e3o linear?<\/h2>\n
      O modelo de regress\u00e3o linear \u00e9 poderoso para a an\u00e1lise preditiva, permitindo determinar a for\u00e7a das vari\u00e1veis categ\u00f3ricas ou independentes, prever o efeito dessas vari\u00e1veis e identificar tend\u00eancias nos dados. <\/p>\n
      Como voc\u00ea pode deduzir pelo nome regress\u00e3o linear<\/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
      Treinamento do nosso modelo de regress\u00e3o linear<\/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. Crie boxplots correspondentes aos valores de cada vari\u00e1vel categ\u00f3rica para detectar valores discrepantes:
        \n
        \n        \"Detect<\/a><\/p>\n<\/li>\n
      6. Crie gr\u00e1ficos de dispers\u00e3o para cada vari\u00e1vel categ\u00f3rica em rela\u00e7\u00e3o \u00e0 vari\u00e1vel-alvo para determinar o grau de correla\u00e7\u00e3o.
        \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 M\u00e9todo dos m\u00ednimos quadrados ordin\u00e1rios<\/a>.
        \n
        \n        \"Ordinary<\/a><\/p>\n<\/li>\n
      8. Agora, treine o modelo usando o c\u00f3digo que voc\u00ea v\u00ea aqui:
        \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
        Indo al\u00e9m com o aprendizado de m\u00e1quina e o Couchbase<\/h2>\n
        Agora que voc\u00ea j\u00e1 se familiarizou com a conex\u00e3o Servidor Couchbase<\/a> ao Jupyter Notebook e explorou o conceito de aprendizado de m\u00e1quina da regress\u00e3o linear, desenvolva esse conhecimento com estas postagens sobre como usar o Couchbase como um armazenamento de modelos de aprendizado de m\u00e1quina<\/a> e possibilitando insights orientados por IA usando o Couchbase<\/a>.<\/p>\n
         <\/div>\n
        Experimente voc\u00ea mesmo:

        \n        Obtenha o Couchbase 7 aqui<\/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 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