With the CPI US at a current level of 300.54, Consumers are currently paying a hefty premium for groceries. Consumers may have many aspects of their day to day lives that affect their grocery spending habits, including occupation, age, gender, etc. With the imported dataset, I believe I can use the Features importance Machine Learning tool to generate a prediction as to which variables have the strongest correlation. Additionally, I feel that it would be best to group the data into sets based on occupation. I feel that certain occupations display certain trends; for example, a consumer working in healthcare might watch more closely what they eat, providing a more costly eating habit. By doing so, a can more clearly use Machine Learning to generate more unbiased conclusions.
import pandas as pd
df_customers = pd.read_csv('customers.csv')
customers_results = {'CustomerID': 'Identification', 'Gender': 'Gender of a customer', 'Age': 'Age of a customer', 'Annual Income ($)': 'Annual income of a customer', 'Spending Score (1-100)': 'Score assigned by the shop, based on customer behavior and spending nature', 'Profession': 'Proffesion of a customer', 'Work Experience': 'In years', 'Family Size': 'Family members of a customer'}