<jupyter_start><jupyter_text>AI in Bussines - Insurance Prediction SageMaker AutoPilot  ---   [![Linkedin: JesusSantana](https://img.shields.io/badge/-JesusSantana-blue?style=flat-square&logo=Linkedin&logoColor=white&link=https://www.linkedin.com/in/chus-santana/)](https://www.linkedin.com/in/chus-santana/) [![GitHub JesusSantana](https://img.shields.io/github/followers/jesussantana?label=follow&style=social)](https://github.com/jesussantana)    Import Dataset<jupyter_code>import pandas as pd

# read the csv file 
insurance_df = pd.read_csv('insurance_data.csv')
insurance_df.head()<jupyter_output><empty_output><jupyter_text>Upload the data to S3 bucket<jupyter_code>import sagemaker


# Creating Sagemaker session using Sagemaker SDK
sess = sagemaker.Session()

#Prefix that we want to use

prefix = 'modern-ai-in-business/input'

#Uploading the data to S3 bucket
uri = sess.upload_data(path = 'insurance_data.csv', key_prefix = prefix)

print(uri)
# location to store the output

output = "s3://sagemaker-us-east-2-563075714756/modern-ai-in-business/output"

print(output)<jupyter_output>s3://sagemaker-us-east-2-563075714756/modern-ai-in-business/output
{'fraction_non_alphanumeric': 0.13671539122957868, 'fraction_numerical': 0.025795356835769563, 'mean_word_length': 6.054545454545455, 'pattern_counts': {'":': 0, '<': 8, '<?xml version=': 0, '>': 8, 'https://': 5, 'lorem ipsum': 0, 'www.': 2, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '474608', 'license': 'MIT', 'n_tokens_mistral': 459, 'n_tokens_neox': 421, 'n_words': 65, 'path': 'notebooks/AI in Business - Insurance Premium with AWS SageMaker AutoPilot/Insurance_Prediction_AutoPilot_Startup.ipynb', 'repo_name': 'jesussantana/Modern-Artificial-Intelligence', 'size': '1163'}