Gaia Data Set for Stellar Classification Assignment

Assignment Task

Scenario

A global space astrometry mission, Gaia is building the largest, most precise three-dimensional map of our Galaxy by surveying nearly two billion objects. Throughout the course of its mission, Gaia monitors each of its target stars about 14 times per year. It is precisely charting their positions, distances, movements, and changes in brightness. It is expected to discover hundreds of thousands of new celestial objects, such as extra-solar planets and brown dwarfs, and observe hundreds of thousands of asteroids within our own Solar System. The mission is also studying more than 1 million distant quasars and providing stringent new tests of Albert Einstein’s General Theory of Relativity. 

In this task, the Head of the Analytics Unit asks you to use Gaia observation to do 2-class (A,B) stellar classification to help understand the Galaxy. As you will see, this dataset is highly complicated and includes a lot of features that make this problem more challenging.

 Your tasks include:

• Understanding the specifics of the dataset; 
• Extracting information about each of the attributes, possible associations between them, and any other specifics of the dataset

Datasets

For this dataset, you only have the attribute headings and a brief description, which you can find hereLinks to an external site.. Each student is assigned an individual table with the actual values of these attributes. You will find your individual dataset in the link below. Your dataset is the one with your student ID in the file name. 

Tasks

A. Initial data exploration

1. Identify the attribute type of each attribute in your dataset. If it's not clear, you may need to justify why you chose the type.

2. For each attribute, conduct below studies for each of them: Identify the values of the summarising properties for the attributes, including frequency, location and spread (e.g. value ranges of the attributes, frequency of values, distributions, medians, means, variances, percentiles, etc. - the statistics that have been covered in the lectures and materials given). Note that not all of these summary statistics will make sense for all the attribute types, so use your judgement! Where necessary, use proper visualisations for the corresponding statistics.

3. Using KNIME or Python, explore multiple attributes relationship of your dataset, and identify any outliers, clusters of similar instances, "interesting" attributes and specific values of those attributes. Note that you may need to 'temporarily' recode attributes to numeric or from numeric to nominal. The report should include the corresponding snapshots from the tools and an explanation of what has been identified there.

B. Data preprocessing

Perform each of the following data preparation tasks (each task applies to the original data) using your choice of tool:

1. Use the following binning techniques to smooth the values of the following two attributes:

• - RA_ICRS
• - DE_ICRS

For each attribute, you must apply:

1. Equi-width binning
2. Equi-depth binning

In the assignment report, for each of these techniques, you need to illustrate your steps. In your Excel workbook file place the results in separate columns in the corresponding spreadsheet. Use your judgement in choosing the appropriate number of bins - and justify this in the report.

2. Use the following techniques to normalise the following attribute:

• - Age-Flame

For this attribute, you must apply:

1. min-max normalization to transform the values onto the range [0.0-1.0].
2. z-score normalization to transform the values.

The assignment report provides an explanation of each of the applied techniques. In your Excel workbook file place the results in separate columns in the corresponding spreadsheet.

3. Discretise the Mass-Flame attribute into the following categories:

• Small (0 - 2)
• Medium (2 - 4)
• Large (4- 8)

Provide the frequency of each category in your dataset.

Your assignment report should provide an explanation of each of the applied techniques. In your Excel workbook file place the results in a separate column in the corresponding spreadsheet.

4. Binarise the SpType-ELS variable [with values "0" or "1"].

Your assignment report should provide an explanation of the applied binarisation technique. In your Excel workbook file place the results in separate columns in the corresponding spreadsheet.

C. Summary

At the end of the report include a summary section in which you summarise your findings. The summary is not a narrative of what you have done, but a condensed informative section of what you have found about the data that you should report to the Head of the Analytics Unit. The summary may include the most important findings (specific characteristics (or values) of some attributes, important information about the distributions, some clusters identified visually that you propose to examine, associations found that should be investigated more rigorously, etc.).

This Data Analysis has been solved by our PhD Experts at My Uni Paper.