Программисты — удалённая работа в Москве

Data Science. Доработка существующего продукта. 9. For two variables, calculate the outliers using both the interquartile range and the standard deviation from the mean. Are the results different? Interpret them. 10. If there are outliers, delete them (if there is a lot of data) or replace them with the mean/median (if there is little data) and see how the measures of the central tendency in the variable under consideration have changed. 11. If there are missing values in the data, specify in which variables and how many of them. And fill them with the median/mean. 12. Build a correlation matrix (use seaborn.heatmap function) based only on those features for which the correlation can be calculated (If there are a lot of such pairs, build at least 10 ) 13. Interpret each correlation value in the matrix between two features 14. Plot the scatter plots based on these features (hint sns.pairplot()) 15. Download your .ipynb file and dataset by the submission form.