Visualize the stars! (Spring 2023)

Visualize the stars! - Analysis of Dataset of Stars

Source from this data set from Kaggle

240 stars and various properties taken from "Stars and Galaxies" by Seeds and Backman.

7 properties of a star, such as its temperature, luminosity, radius, absolute magnitude, star type, star color, and spectral class.

Please note that the star type, denoted as integers, are translated as the following:

• Brown Dwarf -> Star Type = 0
• Red Dwarf -> Star Type = 1
• White Dwarf -> Star Type = 2
• Main Sequence -> Star Type = 3
• Supergiant -> Star Type = 4
• Hypergiant -> Star Type = 5

Since the topic is stars, I shall visualize the stars! (through graphs)

The general aim is to see any correlations between the categories, and that is reinforced through the two paths I took to get to regression.

Tools I used are: Python, Pandas, Matplotlib, Seaborn

I did several methods of correlation,

1. brute force looping through columns of the dataframe to run the correlation methods,
2. seaborn-enabled correlation and regression for all columns (bulk in one go)

Path 1.

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Path 2.

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Extra: I also did pseudo-Hertzsprung-Russell Diagram. With Temperature as x-axis, Luminosity as y-axis. Size of stars (tweaked with logarithm to scale better fitted for a graph) as size of dots and colors of dots (in a gradient), to better showcase the relative sizes of stars on a 2D graph.

Other diagrams I did with combinations of a selected value of each of the remaining properties, in a graph with 3 attributes in one 2D graphs.

Stretch goals

1. Implement colors attributes from dataset to plot on graphs (2D and 3D)

Color category was too hard to work with with compound colors string such as 'Yellowish', 'Orange-Red', and 'Blue-white', which would be a reach goal, to analyze the string and break them down into 2 colors, and then take an average of the colors, then aggregate that for each star entry, to use in the parameter of scatter plot function

2. multivariable regression, for 3D graphs, for every combinations of numerical columns

3. properly replicate the Hertzsprung-Russell Diagram like this image, with multiple regressions lines, one for each of the star types datas, and all in one graph, and display 4 axes with colors of the stars and size of stars scaled with size of dots

Links I consulted

https://sparkbyexamples.com/pandas/pandas-correlation-of-columns/

https://www.w3schools.com/python/pandas/default.asp

https://www.geeksforgeeks.org/matplotlib-tutorial/

https://www.w3schools.com/python/matplotlib_scatter.asp

https://matplotlib.org/stable/tutorials/introductory/pyplot.html#sphx-glr-tutorials-introductory-pyplot-py