Machine Learning. Seaborn Exercises

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The Data We will be working with a famous titanic data set for these exercises. Later on in the Machine Learning section of the course, we will revisit this data, and use it to predict survival rates of passengers. For now, we'll just focus on the visualization of the data with seaborn: https://github.com/mwaskom/seaborn https://seaborn.pydata.org/	Image: Screen Shot 2018 06 13 At 17.57.28 (binary/octet-stream)
Recreate the plots below using the titanic dataframe. There are very few hints since most of the plots can be done with just one or two lines of code and a hint would basically give away the solution. Keep careful attention to the x and y labels for hints. Note! In order to not lose the plot image, make sure you don't code in the cell that is directly above the plot, there is an extra cell above that one which won't overwrite that plot!	Image: Screen Shot 2018 06 13 At 18.02.03 (binary/octet-stream)
joint plots	Image: Screen Shot 2018 06 13 At 18.01.54 (binary/octet-stream)
distplot	Image: Screen Shot 2018 06 13 At 18.02.03 (binary/octet-stream)
boxplot	Image: Screen Shot 2018 06 13 At 18.02.09 (binary/octet-stream)
swarmplot	Image: Screen Shot 2018 06 13 At 18.02.16 (binary/octet-stream)
countplot	Image: Screen Shot 2018 06 13 At 18.02.23 (binary/octet-stream)
heatmap ... rugplot pairplot	Image: Screen Shot 2018 06 13 At 18.02.32 (binary/octet-stream)
FacetGrid	Image: Screen Shot 2018 06 13 At 18.16.48 (binary/octet-stream)

Zusammenfassung der Ressource

	Erstellt von María Marchante vor etwa 6 Jahre