Explain the reasoning for selecting one of the following machine learning approaches: Machine Learning Assignment, DBS, Ireland

Machine learning for Data Analytics:

1. Explain the reasoning for selecting one of the following machine learning approaches for the chosen dataset (supervised/ unsupervised/ semi-supervised). Discuss and explain the rationale for choosing the appropriate project management framework/ activities (CRISP-DM, KDD or SEMMA).

2. Machine learning models have a wide range of uses, including prediction, classification, and clustering. It is advised that you assess several approaches (at least two), choose appropriate parameters based on hyperparameters, and then analyze the chosen approaches.

3. Perform the training and testing of the machine learning models, with cross-validation/ Research, to demonstrate the authenticity of the modelling outcomes. Display a comparison of the results of two or more ML modelling using a table or graph representation. Examine the performance of the machine learning models based of the chosen metric for supervised/ unsupervised/ semi-supervised approaches and analyze it critical.

4. Demonstrate the similarities and differences between your Machine Learning modelling results using the tables or visualizations. Provide a report along with an explanation and interpretation to convince DCC of the relevance and effectiveness of your findings.

Programming:  The project must be explored programmatically, this means that you must implement suitable Python tools (code and/or libraries) to complete the analysis required. All of this is to be implemented in a Jupyter Notebook. Your codebook should be properly annotated. The project documentation must include sound justifications and explanation of your code choice.