abhijeet-talaulikar

# Step 1: Convert all interactions to a list
journeys = campaign_data.groupby('customer_id')['channel'].aggregate(
    lambda x: x.tolist()).reset_index()

# Step 2: Add last interaction as 1 or 0 event representing activation
activation_results = campaign_data.drop_duplicates('customer_id', keep='last')[['customer_id', 'activation']]
journeys = pd.merge(journeys, activation_results, how='left', on='customer_id')

# Step 3: Add start and end states based on whether customer activated
journeys['path'] = np.where(

abhijeet-talaulikar

import numpy as np
import pandas as pd

campaign_data = pd.read_csv("cashback_activation_data.csv")

campaign_data = campaign_data.sort_values(['customer_id', 'timestamp'],
                    ascending=[False, True])

campaign_data['visit_order'] = campaign_data.groupby('customer_id').cumcount() + 1

robshox

## LAMNDA FUNCTION lambda_function.py

import boto3
import json

# grab environment variables
ENDPOINT_NAME = "jumpstart-dft-meta-textgeneration-llama-2-7b-rs"
runtime= boto3.client('runtime.sagemaker')

def lambda_handler(event, context):

Sandy4321

avidale

import numpy as np
import scipy.optimize
from sklearn.linear_model import Ridge
from sklearn.isotonic import IsotonicRegression
from sklearn.base import BaseEstimator, RegressorMixin


class MonotonicRegression(BaseEstimator, RegressorMixin):
    """ Smooth increasing piecewise linear regression.
    During training, it minimizes MSE and the sum of absolute changes in its slope.

jdmoore7

import pymc3 as pm
from theano import tensor as tt
import arviz as az
import numpy as np

# Binary, correct answer array
scores = np.array([1,1,1,0,0,0
    ]).flatten()

# (student:question) tuples

avidale

avidale

avidale

JulianaFontolan

# Call the estimator with the main parameters

from sagemaker.estimator import Estimator

estimator = Estimator(
    image_uri=ecr_image,
    role=role,
    instance_count=1,
    instance_type="ml.c4.xlarge",
    output_path=out_path