Feature engineering

Winning a Kaggle Competition in Python

Yauhen Babakhin

Kaggle Grandmaster

Solution workflow

solution workflow

Modeling stage

modeling stage components

Modeling stage

modeling stage components

Modeling stage

modeling stage components

Feature engineering

feature engineering scheme

Feature engineering

feature engineering scheme

Feature types

Numerical
Categorical
Datetime
Coordinates
Text
Images

Creating features

# Concatenate the train and test data
data = pd.concat([train, test])

# Create new features for the data DataFrame...

# Get the train and test back
train = data[data.id.isin(train.id)]
test = data[data.id.isin(test.id)]

Arithmetical features

# Two sigma connect competition
two_sigma.head(1)

      id     bathrooms  bedrooms price interest_level
0     10     1.5        3        3000  medium

# Arithmetical features
two_sigma['price_per_bedroom'] = two_sigma.price / two_sigma.bedrooms
two_sigma['rooms_number'] = two_sigma.bedrooms + two_sigma.bathrooms

Datetime features

# Demand forecasting challenge
dem.head(1)

          id           date store  item  sales
0     100000     2017-12-01     1     1     19

# Convert date to the datetime object
dem['date'] = pd.to_datetime(dem['date'])

Datetime features

# Year features
dem['year'] = dem['date'].dt.year

# Month features
dem['month'] = dem['date'].dt.month

# Week features
dem['week'] = dem['date'].dt.weekofyear

# Day features
dem['dayofyear'] = dem['date'].dt.dayofyear
dem['dayofmonth'] = dem['date'].dt.day
dem['dayofweek'] = dem['date'].dt.dayofweek

date           year  month   week
2017-12-01     2017     12     48
2017-12-02     2017     12     48
2017-12-03     2017     12     48
2017-12-04     2017     12     49

date      dayofyear  dayofmonth dayofweek
2017-12-01  335      1          4
2017-12-02  336      2          5
2017-12-03  337      3          6
2017-12-04  338      4          0

Let's practice!

Winning a Kaggle Competition in Python