DAT7303: In portfolio 3, students must complete 4 tasks related to analysing a dataset using CRISP- DM methodology: Data Mining Course Work,

Portfolio Brief

In portfolio 3, students must complete 4 tasks related to analysing a dataset using CRISP- DM methodology. The studentsshould clearly mention all the = steps undertaken, from importing data, cleaning, preparing data, and analyzing data. The students must apply appropriate predictive analytical techniques, provide appropriate visualisation/s, and report the analysis results. Data cleaning, preparation, predictive analysis, and visualisations must be performed in R programming.

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All the steps/code/results must be shared. Your portfolio report should be produced in a professional report style and should contain at least 1500 words, excluding the references and appendices sections.

Task 1
This is the Step 1 and 2 of CRISP-DM. Any good project starts with a deep understanding of the customer’s needs. In this portfolio task, students are required to prepare a report covering the following needs.

The Business Understanding phase focuses on understanding the objectives and requirements of the project. While many teams hurry through this phase, establishing a strong business understanding is like building the foundation of a house.

Determine business objectives: You should first “thoroughly understand, from a business perspective, what the customer really wants to accomplish.” and then define business success criteria.

Assess the situation: Determine resources availability, project requirements, assess risks, and contingencies, and conduct a cost-benefit analysis. Determine data mining goals: In addition to defining the business objectives, you should also define what success looks like from a technical data mining perspective.

Produce project plan: Select technologies and tools and define detailed plans for each project phase. The next step of CRISP-DM is the Data Understanding phase. Adding to the foundation of Business Understanding, it drives the focus to identify, collect, and analyse the data sets that can help you accomplish the project goals. This phase also has four tasks: Collect initial data: Acquire the necessary data and (if necessary) load it into your analysis tool.

Describe data: Examine the data and document its surface properties like data format, number of records, or field identities.  Explore data: Dig deeper into the data. Query it, visualize it, and identify relationships among the data. Verify data quality: How clean/dirty is the data? Document any quality issues.

Task 2

This is the Step 3 of CRISP-DM. This phase, which is often referred to as “data munging”, prepares the final data set(s) for modelling. A common rule of thumb is that 80% of the project is data preparation.

In this step, students are required to prepare data to apply predictive techniques in the next step. Students are also required clearly demonstrate each step of the data preparation with justifications. In general, there are five tasks in step 3 of CRISP-DM.

Select data: Determine which data sets will be used and document reasons for inclusion/exclusion. Clean data: Often this is the lengthiest task. Without it, you’ll likely fall victim to garbage-in, garbage-out. A common practice during this task is to correct, impute, or remove erroneous values.

Construct data: Derive new attributes that will be helpful. For example, derive someone’s body mass index from height and weight fields.

Integrate data: Create new data sets by combining data from multiple sources. Format data: Re-format data as necessary. For example, youmight convertstring values that store numbers to numeric values so that you can perform mathematical operations.

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Task 3
This is the Step 4 of CRISP-DM. This stage is widely regarded as data science’s most exciting work and is often the shortest phase of the project. In this step, students are required to build and assess various models based on several different modelling techniques. This phase hasfour tasks: Select modelling  techniques: Determine which algorithms to try (e.g. regression, classification, neural net).

Generate test design: Pending the modelling approach, it is required to split the data into training, test, and validation sets.
Build model: This might just be executing a few lines of code like “reg =
LinearRegression().fit(X, y)”. Assess model: Generally, multiple models are competing against each other, and the data scientist needs to interpret the model results based on domain knowledge, the pre- defined success criteria, and the test design.

Although the CRISP-DM Guide suggests to “iterate model building and assessment until you strongly believe that you have found the best model(s)”, in practice teams should continue iterating until they find a “good enough” model, proceed through the CRISP-DM lifecycle, then further improve the model in future iterations.

In this task, students are required to build models (and assess them) with a minimum of 3 predictive algorithm techniques. Students are allowed to select any three methods but are recommended to use machine learning models. Some examples of predictive modelling algorithms are given below.

Statistical models: ARIMA autoregressive integrated moving average, VAR vector autoregression, VECM vector error correction model, AFE arbitrage of forecasting experts Machine Learning models: SR standard regression, SVR support vector regression, DT decisions tree, RF random forest Deep Learning models: LSTM long short-term memory, TCN temporal convolutional
network) Moreover, students must have in-depth knowledge about the selected predictive algorithm techniques. In the report, students are required to show a theoretical understanding of each selected predictive algorithm. In this task, students should also evaluate the models that have been created and come up with the optimal model.

Task 4
Evaluation isthe Step 5 of CRISP-DM. Whereasthe Assess Modeltask ofthe Modelling phase focuses on technical model assessment, the Evaluation phase looks more broadly at which model best meets the business and what to do next. This phase has three tasks: Evaluate results: Do the models meet the business success criteria? Which one(s) should we approve for the business?
Review process: Review the work accomplished. Was anything overlooked? Were all steps properly executed? Summarize findings and correct anything if needed.

Determine nextsteps: Based on the previousthree tasks, determine whetherto proceed to deployment, iterate further, or initiate new projects. Deployment is Step 6 (last step) of CRISP-DM. A model is not particularly useful unless the = customer can access its results. The complexity of this phase varies widely.

This final phase has four tasks:
Plan deployment: Develop and document a plan for deploying the model. Plan monitoring and maintenance: Develop a thorough monitoring and maintenance plan to avoid issues during the operational phase (or post-project phase) of a model.

Produce final report: The project team documents a summary of the project, which might include a final presentation of data mining results.
Review project: Conduct a project retrospective about what went well, what could have been better, and how to improve in the future.

As a project framework, CRISP-DM does not outline what to do after the project (also known as “operations”). However, if the model is going to production, be sure you maintain the model in production. Constant monitoring and occasional model tuning are often required.

In this task, students must complete step 5 of CRISP-DM (i.e. evaluation). Students are not required to complete step6 (i.e., deployment). Students getmarksforthe data/results visualisations as appropriate in Task 1, Task 2, Task 3, and Task4.

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