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Start by initializing the environment by importing the modules and getting the default S3 bucket used by SageMaker Studio. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
import sagemaker import boto3 import pandas as pd from sagemaker import get_execution_role region = boto3. Session (). region_name session = sagemaker. Session () bucket = session. default_bucket () print ( bucket) prefix = 'sagemaker/termdepo' role = get_execution_role () sm = boto3. client ( service_name = 'sagemaker', region_name = region)
Let's start by downloading the bank marketing dataset from which contains historical data of customers buying decisions of a new term deposit. Feel free to explore the schema of the dataset explained at Datahub. The last column (Class) has a value of 1 or 2 where 1 represents a no and 2 represents a yes. Our goal is to train a model that can predict customer decisions based on historical data.! wget - N: // local_data_path = ''
We will now split the data for training (80%) and testing (20%).
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