Rstudio software

Deliverables

Each project will be somewhat different. However, the expected deliverables should minimally include the following. First, submit a Word file that has the following sections.

I. Known: Describe what you know about your (in a bulleted format). This includes the dataset used.

II. Visualize: Key to any project is a visualization of the provided data.  The visualization should be an aid to how you look at the data and should potentially inform what your goals are.

III. Goals: Describe the end goal(s) in a bulleted format. Please be sure to justify the specific human thermal comfort conditions you will be seeking in your design.

IV. Factors: Define factors (input and target) considered in your analysis.

V. Design Space: Establish the design space for your model. Ideally this will include histograms of the input or predictor variables to establish the valid space for models created.

VI. Outliers (if applicable): Identify outlier data points. These data points should be moved from your model data. An outlier plot is essential.

VII. Factor Importance: Present results ranking the input factors in terms of the influence on the final model. It is important for your final model to include only input factors that actually influence the goodness of the model. 

VIII. Model: Present model results; ideally showing the quality of your model predictions (both visually and through statistical metrics, e.g, r²). You should present validation results that show how your models have improved through use of different data-mining approaches or variants of one approach. For example, if outliers are removed, you should show the value of doing so in terms of the model results.

IX. Conclusions: Summarize information created from your data mining. 

X. Individual Hot-Wash: Describe what you learned from this project. Describe also if you will consider using R-based data-mining in your future career.

Submission

In one zip file, include the following:

·         Word File addressing the deliverables.

·         R-script file(s) used in your analysis. Please be sure to comment your script.

·         R-data files with saved input and processed data.