Execution of machine learning model solution
Submit a report detailing the execution plan of a machine learning model solution for your chosen scenario (listed at the bottom). The report will include an evaluation plan of model result accuracy, detailing possible model parameter changes for the identification of recommended parameter values. Additionally, the report should include an analysis of the possible 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 do your 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?
Case Study
Dataset name: Building People Counts Data
Dataset location: https://archive.ics.uci.edu/ml/machine-learning-databases/event-detection/CalIt2.data; https://archive.ics.uci.edu/ml/machine-learningdatabases/event-detection/CalIt2.events
Dataset attributes: 10080 observables of 4 variables; observations from two data streams: people flow in and out of a building
Dataset info: https://archive.ics.uci.edu/ml/datasets/CalIt2+Building+People+Counts
Problem: Predict the occurrence of a conference event in the building
Submit a report detailing the execution plan of a machine learning model solution for your chosen scenario (listed at the bottom). The report will include an evaluation plan of model result accuracy, detailing possible model parameter changes for the identification of recommended parameter values. Additionally, the report should include an analysis of the possible 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 do your 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?
Case Study
Dataset name: Building People Counts Data
Dataset location: https://archive.ics.uci.edu/ml/machine-learning-databases/event-detection/CalIt2.data; https://archive.ics.uci.edu/ml/machine-learningdatabases/event-detection/CalIt2.events
Dataset attributes: 10080 observables of 4 variables; observations from two data streams: people flow in and out of a building
Dataset info: https://archive.ics.uci.edu/ml/datasets/CalIt2+Building+People+Counts
Problem: Predict the occurrence of a conference event in the building