Reference no: EM133163318

MLN 601 Machine Learning - Torrens University Australia

Assessment - Regression Analysis

Learning Outcome 1: Apply learning algorithms to perform machine learning tasks.

Learning Outcome 2: Implement practical machine learning: data pre-processing, analysis, model selection, and interpret the results.

Learning Outcome 3: Communicate clearly and effectively using the technical language of machine learning to a range of stakeholders

Task Summary

In this Assessment, you will use a linear regression Machine Learning (ML) algorithm to analyse data and draw conclusions. To help you create and document the ML model and the results, you will follow the end-to-end CRoss-Industry Standard Process for Data Mining (CRISP-DM) (Chapman et al., 2000) methodology. Further, to guide you through the analysis and the writing of the report, a template for your Jupyter Notebook has been provided.

The CRISP-DM template is a necessary resource for the completion of this Assessment. You must consult the CRISP-DM template for further details and information.

Context
In this Assessment, you will complete an end-to-end ML exercise using real-world data. In your future workplaces, you will often be expected to undertake similar exercises using suitable data sets. The template and the use of a methodology will ensure that you do not simply perform an analysis and present your results; rather, you will be able to share the output of the analysis and discuss why and how you adopted the methodology with your work colleagues.

For this Assessment, the practice data set is available from the UCI ML repository, which contains nearly 500 real-world data sets. Your focus will be on the wine quality data set. This data set provides wine quality data across 11 traits, including acidity, residual sugar and alcohol concentration. Importantly, this Assessment requires you to develop a model to predict wine quality on a score between 1 to 10.

You must consider the CRISP-DM from the outset. As the report template indicates, the first stage is Business Understanding. This stage requires consideration of the project problem. For example, it may be easiest to focus on predicting only the quality of the red wine rather than that of both the red and white wine. Ensure that you clearly document whatever target you select for the problem at the commencement of your report (as per the template).

Task Instructions

You will use your Jupyter Notebook on the Microsoft Azure ML platform or Google Colab and Python 3.6 as the language for all three assessments.

Ultimately, the Notebook will contain both your ML code, data and report documentation.

Your Assessment will be evaluated based on the major stages of the CRISP-DM process as set out in the Notebook template with prompts. The process comprises:
1. Business Understanding;
2. Data Understanding;
3. Data Preparation;
4. Modelling;
5. Evaluation; and
6. Deployment.

The six multi-step stages of the CRISP-DM must be undertaken to complete this Assessment. Note: For ease of working and to complete this Assessment, you should document what you are doing in your Notebook as you progress through the activities (e.g., the steps undertaken and the rationale for the selection of the code). The template will prompt you on how to work through the end-to-end ML process.

Stage 1: Business Understanding
1. This section serves as an introduction. You should write a clear and concise narrative, expressing what you are trying to achieve with regards to your evaluation criteria. Think in terms of ML; for example, the prediction algorithm, the data set selected, what you are seeking from the data set and how you intend to understand the value of your prediction capability.
2. Assess the current situation. See 1.1 of the CRISP-DM template (1.1).

Stage 2: Data Understanding
1. Acquire the relevant wine quality data set from the UCI repository for your prediction model. Explicitly specify the data source by providing a specific link and the name of the data set (e.g., red wine, white wine or both) and the method of acquisition (e.g., direct from the URL or a download of the .csv file). The steps taken need to be clearly stated. (2.1).
2. Read this data set into your Notebook. (2.1).
3. Describe the data set inclusive of variables, units and levels. (2.2).
4. Verify the data quality by analysing the data set for structure and missing data. (2.3).
5. Conduct an initial data exploration using data visualisation, reporting and querying the data. (2.4).
6. Use the pairplot function in seaborn to determine the relationship, if any, between the variables. Include the output or the visualisation of the pairplot function in your Notebook and comment on it. (2.4.2).

Stage 3: Data Preparation
1. Select the data that you will use for the analysis. (3.1).
2. Clean the data you have selected to improve the quality of the data. (3.2).

Stage 4: Modelling
1. For this Assessment, you are required to use the linear regression model.
2. Import the linear regression model into your code. (4.1).
3. Record any modelling assumptions. (4.2).
4. Run your model over the data set. (4.3).
5. Record the parameter settings, your rationale for your choice of values and the actual model generated. (4.3).
6. Revise any parameter settings for subsequent model runs. Document all the revisions until the best model is reached. (4.4).

Stage 5: Evaluation
Assess the ML results. Ensure you include a statement as to whether the model meets the evaluation criteria.

Stage 6: Deployment
For this Assessment, you are not required to deploy your model. For this stage, simply include any lessons that you learned and that you wish to share in relation to the things that went right and wrong, the areas in which you did well and in which you could improve. You can also detail any of your other experiences in completing this Assessment.

Attachment:- Machine Learning.rar