Load the dataset and appropriately define data types. Verify data quality: Explain any missing values or duplicate data. Visualize entries that are missing/complete for different attributes.
Visualize attribute distributions. Choose and visualize distributions for a subset of single attributes. Choose any appropriate visualization such as histograms, kernel density estimation, box plots, etc. Visualize relationships between a subset of attributes. Use whichever visualization method is appropriate for your data. Explain any interesting relationships. Important: Interpret the implications for each visualization.
implement dimensionality reduction using t-SNE, then visualize and interpret the results. Give an explanation of t-SNE dimensionality reduction methods.