Submit a report detailing the execution of a machine learning model solution for your chosen scenario. The report will include evaluation of model result accuracy, including model parameter changes and identification of recommended parameter values. Additionally, the report should include an analysis of the model results for any organizational impact. Specifically, the following critical elements must be addressed:
I. Model Execution
a) Initial Execution: Run/train the model with the given initial parameters on the test dataset and explain your process and results. (Note: You should include your results in your report, formatted in the manner most appropriate for clarity.)
b) Initial Evaluation: Evaluate these initial conditions compared to the validation dataset from the scenario and your expectations of the results. How does the results meet up with the validation dataset?
c) Parameter Changes: Change parameters to investigate the effects to the model accuracy. How did changing the parameters impact your results? Are there trends or patterns that you notice?
d) Parameter Confirmation: Identify the parameters you feel are best for this model and dataset and explain what led you to this conclusion. Be sure to explain your reasons clearly, calling on specific examples to assist your explanation.
e) Organizational Impact: Given your parameter selection and model execution, what are the impacts on the organizational task or problem at hand? How could the model and results be used to impact the organization?
C:UsersDgoncalvesDesktopUCI Machine Learning Repository_ Poker Hand Data Set.html
Using regression model in R
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