feature-engineering-guide

Follow this guide to learn feature engineering with python for machine learning.
Here I will be covering how to deal with numerical and categorical data.

contents:

Notebooks 📕

• feature-engineering-guide-part1.ipynb
• feature-engineering-problem.ipynb

Datasets 📁

• data.csv

Case Summary

An International Public Health Institution has run a survey with 3000 respondants divided equally
across 7 major cities around the world. The Result's have been compiled and provided to you in the
form a table. Following are the features as per the received response : -

1. id
2. name
3. age
4. marriage_status
5. gender
6. weight
7. height
8. hours_of_exercise
9. avg_daily_calories
10. diet_type
11. if_smokes
12. if_drinks
13. if_drugs
14. city
15. city_temperature
16. diseases_or_conditions
17. income
18. education_level

Your task is to come up with more information, characteristics and features.