Brainome assumes your CSV file has certain characteristics:
Use these parameters to change our assumptions.
This notebook assumes brainome is installed as per notebook brainome_101_Quick_Start
!python3 -m pip install brainome --quiet
!brainome -version
import urllib.request as request
response1 = request.urlretrieve('https://download.brainome.ai/data/public/bank.csv', 'bank.csv')
print(" Headerless data set bank.csv ".center(80,"-"))
!head -4 bank.csv
print("\n"," Ranking an headerless data file ".center(80,"-"))
!brainome bank.csv -headerless -y -o predictor_106_headerless.py | grep -A 6 "Attribute Ranking:"
Brainome assumes the last column is the target.
Use -target
to specify a different column.
In this example, we use titanic_train.csv but rather than predicting Survived, we predict Cabin_Class
!brainome https://download.brainome.ai/data/public/titanic_train.csv -target Cabin_Class -y -o predictor_106_target.py | grep "Target Column:"
Brainome will use all the columns in your data set. Most data sets include unique identifiers to tie the predictions to an external source.
Use -ignorecolumns
to omit features from your model.
In this example, we ignore PassengerId and Ticket_Number from titanic_train.csv
!brainome https://download.brainome.ai/data/public/titanic_train.csv -ignorecolumns "PassengerId,Ticket_Number" -y -o predictor_106_ignorecolumns.py | grep -A 10 "Attribute Ranking:"