Simulation and problem solving using basic machine learning

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Reference no: EM133902834

Artificial Intelligence and Machine Learning

Simulation and problem solving using basic machine learning models

Assessment - Modelling and Simulation

Task
Utilising either Orange Data Mining or Python, develop both supervised and unsupervised basic machine learning models to address real-world problems. Respond to the accompanying questions with thoughtful analysis. While collaboration with your group members is encouraged, you must independently design and implement your machine learning workflows and provide your answers.

Assessment Description
Throughout our exploration of machine learning, we have covered fundamental models such as logistic regression, k-NN, decision trees, and k-means clustering. These models serve as the foundation for building analytics workflows using tools like Orange Data Mining or Python.
In this assessment, you will apply these basic machine learning models to real-world datasets, demonstrating their practical use in solving real-world problems (SLO 1). You will analyse the characteristics of each model, assess their effectiveness, and engage in problem-solving exercises that challenge your understanding (SLO 2). Access assignment help for any subject instantly.

Assessment Instructions
Group (In-Class Activity):
Working collaboratively in your group, use Orange Data Mining or Python to implement machine learning workflows to solve real-world problems. The datasets, parameters, and instructions are provided in the assessment sheet.
Based on software outputs, answer the accompanying questions in the assessment sheet. Some of the questions might require some manual calculations.

The activity is timed for 1 hour and 30 minutes. Your facilitator will engage with your group, asking questions individually to assess your understanding of the workflows and machine learning models.

Reference no: EM133902834

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