HW7 Using the same data set discussed in DB8 and one tool (RStudio, Python, Jupyter, RapidMiner, or Tabeau), create a model from the unstructured dataset you found online; please cite your sources. Discuss your process and evaluate your results. This assignment should be two pages minimum, double spaced, with APA formatting. Include screenshots where applicable. HW8 Using PowerPoint, create a value proposition for implementing a business intelligence portfolio from a data warehouse. Include graphics to make your proposal more engaging. Your presentation should be a minimum of 10 slides, including the title and references slides.|Essay pro

Posted: February 20th, 2023

HW7

Using the same data set discussed in DB8 and one tool (RStudio, Python, Jupyter, RapidMiner, or Tabeau), create a model from the unstructured dataset you found online; please cite your sources. Discuss your process and evaluate your results.

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This assignment should be two pages minimum, double spaced, with APA formatting. Include screenshots where applicable.

 

HW8

Using PowerPoint, create a value proposition for implementing a business intelligence portfolio from a data warehouse. Include graphics to make your proposal more engaging.

Your presentation should be a minimum of 10 slides, including the title and references slides.

SOLUTION

The process of creating a model from an unstructured dataset typically involves the following steps:

  1. Data preparation: In this step, the unstructured dataset is converted into a structured format so that it can be analyzed. This involves cleaning, transforming, and organizing the data into a format that can be processed by the tool.
  2. Feature extraction: In this step, the most relevant features are extracted from the structured data. This is done to reduce the amount of data that needs to be processed and to ensure that only the most important data is used to build the model.
  3. Model selection: In this step, the appropriate model is selected based on the type of data and the problem being solved. There are many different types of models, such as linear regression, decision trees, and neural networks, each of which is suited for different types of data and problems.
  4. Model training: In this step, the selected model is trained on the prepared and extracted data. This involves adjusting the model’s parameters so that it can accurately predict

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