Waarom features genereren?

Feature engineering voor Machine Learning in Python

Robert O'Callaghan

Director of Data Science, Ordergroove

Feature engineering

Verschillende datatypes

Continu: gehele getallen of floats (decimalen)
Categoraal: beperkte set waarden, bv. gender, geboorteland
Ordinaal: gerangschikte waarden, vaak zonder afstand
Booleaans: True/False
Datetime: datums en tijden

Cursusopzet

Hoofdstuk 1: Featurecreatie en -extractie
Hoofdstuk 2: Werken met rommelige data
Hoofdstuk 3: Normalisatie van features
Hoofdstuk 4: Werken met tekstfeatures

Pandas

import pandas as pd  
df = pd.read_csv(path_to_csv_file)
print(df.head())

Dataset

              SurveyDate  \
0    2018-02-28 20:20:00     
1    2018-06-28 13:26:00     
2    2018-06-06 03:37:00     
3    2018-05-09 01:06:00     
4    2018-04-12 22:41:00    

                              FormalEducation
0    Bachelor's degree (BA. BS. B.Eng.. etc.)
1    Bachelor's degree (BA. BS. B.Eng.. etc.)
2    Bachelor's degree (BA. BS. B.Eng.. etc.)
3    Some college/university study  ...
4    Bachelor's degree (BA. BS. B.Eng.. etc.)

Kolomnamen

print(df.columns)

Index(['SurveyDate', 'FormalEducation',
       'ConvertedSalary', 'Hobby', 'Country',
       'StackOverflowJobsRecommend', 'VersionControl', 
       'Age', 'Years Experience', 'Gender', 
       'RawSalary'], dtype='object')

Kolomtypes

print(df.dtypes)

SurveyDate                            object
FormalEducation                       object
ConvertedSalary                      float64
...
Years Experience                       int64
Gender                                object
RawSalary                             object
dtype: object

Specifieke datatypes selecteren

only_ints = df.select_dtypes(include=['int'])
print(only_ints.columns)

Index(['Age', 'Years Experience'], dtype='object')

Laten we beginnen!

Feature engineering voor Machine Learning in Python