redshiftML.py

import boto3

redshift = boto3.client('redshift')
credentials = redshift.get_cluster_credentials(
    DbUser='demouser', 
    DbName='dev', 
    ClusterIdentifier='redshiftml', 
    DurationSeconds=3600,
    AutoCreate=False
)

tmp_db_user = credentials['DbUser']
tmp_db_password = credentials['DbPassword']

import psycopg2
conn = psycopg2.connect(
    host="<redshift_endpoint_name>", 
    port=5439, 
    dbname='dev', 
    user=tmp_db_user, 
    password=tmp_db_password
)

sql = """
CREATE MODEL demo_ml.customer_churn_model
FROM (SELECT state,
             area_code,
             total_charge/account_length AS average_daily_spend, 
             cust_serv_calls/account_length AS average_daily_cases,
             churn 
      FROM demo_ml.customer_activity
         WHERE record_date < '2020-01-01' 

     )
TARGET churn
FUNCTION predict_customer_churn
IAM_ROLE 'arn:aws:iam::373011628954:role/RedshiftML'
SETTINGS (
  S3_BUCKET 'redshiftml-373011628954',
  MAX_RUNTIME 1800
)
;
END;
"""
with conn.cursor() as cur:
    cur.execute(sql)
    
sql = """
SHOW MODEL demo_ml.customer_churn_model;
"""
with conn.cursor() as cur:
    cur.execute(sql)
    result = cur.fetchall()
    print(result)