Machine Learning - sklearn
Budget: $10 – $30 AUD
Please include a proper Table of Contents.
ntroduction: This section needs to include the following subsections:
Report Overview: A complete and accurate overview of the contents of your Phase 2 report. Clarification: A Table of Contents is not a report overview.
Overview of Methodology: A detailed, complete, and accurate overview of your predictive modelling methodology.
Predictive Modelling: This section needs to include the following subsections:
Feature Selection (FS) as appropriate. We would like to see some meaningful effort for selecting the best descriptive features in your dataset. For FS, there are no hard requirements; you can use any FS method and any number of features you like. However, you need to try at least one FS method and at least one specific number of features. For example, you can simply select 10 features using f_classif (Links to an external site.) for classification problems or f_regression (Links to an external site.) for regression problems.
Model Fitting & Tuning: Details of your ML algorithms’ fine-tuning process and performance analysis of each algorithm. You will also need to include at least one (meaningful) plot showing the results of your hyper-parameter fine-tuning process for each one of your algorithms.
Neural Network Model Fitting & Tuning You will need to fine-tune at least 5 different NN hyperparameters and present at least 5 different fine-tuning plots. In addition, your discussion for the NN model section needs to be at least 600 words that clearly explains your modeling approach in detail. For NN models, you will find the link here (Links to an external site.) on our website useful.
Model Comparison of the algorithms' performance as appropriate (cross-validation, AUC, etc.) using paired t-tests.
NOTE: For Task #2 (Predictive Modelling), your work will be marked on correctness of your methodology, not absolute performance of your models. For instance, you will not lose points if your results indicate a poor performance, but you will need to discuss this in the Critique & Limitations section of your report.
Critique & Limitations of your approach: strengths and weaknesses in detail.
Summary & Conclusions: This section needs to include the following subsections:
Summary of Findings: A comprehensive summary of your findings. That is, what exactly did you find about your particular problem?
Conclusions: Your detailed conclusions as they relate to your goals and objectives.
ntroduction: This section needs to include the following subsections:
Report Overview: A complete and accurate overview of the contents of your Phase 2 report. Clarification: A Table of Contents is not a report overview.
Overview of Methodology: A detailed, complete, and accurate overview of your predictive modelling methodology.
Predictive Modelling: This section needs to include the following subsections:
Feature Selection (FS) as appropriate. We would like to see some meaningful effort for selecting the best descriptive features in your dataset. For FS, there are no hard requirements; you can use any FS method and any number of features you like. However, you need to try at least one FS method and at least one specific number of features. For example, you can simply select 10 features using f_classif (Links to an external site.) for classification problems or f_regression (Links to an external site.) for regression problems.
Model Fitting & Tuning: Details of your ML algorithms’ fine-tuning process and performance analysis of each algorithm. You will also need to include at least one (meaningful) plot showing the results of your hyper-parameter fine-tuning process for each one of your algorithms.
Neural Network Model Fitting & Tuning You will need to fine-tune at least 5 different NN hyperparameters and present at least 5 different fine-tuning plots. In addition, your discussion for the NN model section needs to be at least 600 words that clearly explains your modeling approach in detail. For NN models, you will find the link here (Links to an external site.) on our website useful.
Model Comparison of the algorithms' performance as appropriate (cross-validation, AUC, etc.) using paired t-tests.
NOTE: For Task #2 (Predictive Modelling), your work will be marked on correctness of your methodology, not absolute performance of your models. For instance, you will not lose points if your results indicate a poor performance, but you will need to discuss this in the Critique & Limitations section of your report.
Critique & Limitations of your approach: strengths and weaknesses in detail.
Summary & Conclusions: This section needs to include the following subsections:
Summary of Findings: A comprehensive summary of your findings. That is, what exactly did you find about your particular problem?
Conclusions: Your detailed conclusions as they relate to your goals and objectives.