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1. Data exploration and preparation in RapidMiner

Clean up, transform and filter your data as needed. Replace missing values. Select the required label(s) and predictors. Explore, chart and explain the most important attributes and their significant features.

2. Discovering Relationships and Data Transformation in RapidMiner

Identify data groups and categories with data clustering. Assess cluster quality. Perform segmentation analysis of clustered data. Visualise and explain groups of observations.

3. Create a Model(s) in RapidMiner

Develop at least these model types: a) Regression; b) Decision trees; c) Neural networks. If not formally optimising the models, create multiple versions of these models (at least three each) to identify / select optimal model hyper-parameters.

4. Evaluate and Improve the Model(s) in RapidMiner

Use cross-validation to evaluate the models’ performance. Visualise, interpret and report the results of your performance testing.

5. Provide an Integrated Solution in RapidMiner

In point form, describe the process of using your solution. Create a new data set (e.g. 10 examples) and show with screen shots step-by-step how the process can be applied to this new data.

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