Basics.ipynb (56.1 KB)
Kudos on completing this project. This was quite detailed analysis .
In data science industry it is very important to keep code clean and presentable.
you could add few things to improve your future works.
- Always use Headings. : Use headings and subheadings to make your notebook more presentable.
- Missing value analysis : I have noticed a lot of features have NaN values.
Identifying and treating missing values is one of key steps in a data science project.
df.isnull().sum()
- Proper presentation of distributions:
instead of using
df[βcolβ].value_counts(normalize=True)
use
df[βcolβ].value_counts(normalize=True).mul(100).round(2).astype(βstrβ) + β%β
Thanks a lot - I will try to go over the coding again and try to implement your feedback. Thanks a lot!