Feature engineering

Predicting CTR with Machine Learning in Python

Kevin Huo

Instructor

Dealing with dates

print(df.hour.head(1))

14102101

df['hour'] = pd.to_datetime(
  df['hour'], format = '%y%m%d%H')
df['hour_of_day'] = df['hour'].dt.hour
print(df.hour.head(1))

2014-10-21 01:00:0

print(df.groupby('hour_of_day')
      ['click'].sum())

             click
hour_of_day       
1             1092
2             6546

Converting categorical variables via hashing

Categorical features must be converted into a numerical format
Hash function: maps arbitrary input to an integer output, returning exact same output for a given input
Lambda function: lambda x: f(x)
Apply hash function via f(x) = hash(x) as follows:

df['site_id'] = df['site_id'].apply(lambda x: hash(x), axis = 0)

83a0ad1a -> -9161053084583616050
85f751fd-> 818242008494177460

A closer look at features

Examples of count() and nunique():

df['ad_type'].count()

df['ad_type'].nunique()

Example of distribution of a categorical column

Creating features

Most of variables are categorical
Adding more features is better for predictive power
Example of new feature: impressions by device_id (user) and search_engine_type:

df['device_id_count'] = df.groupby('device_id')['click'].transform("count")
df['search_engine_type_count'] = df.groupby('search_engine_type')['click'].transform("count")
print(df.head(1))

...  device_id_count  search_engine_type_count
...            40862                     47710

Let's practice!

Predicting CTR with Machine Learning in Python