This project presents a prediction model designed to estimate a customer's Spending Score, a metric commonly used in marketing analytics to understand purchasing behavior and target customer segments. The goal is to identify which customer characteristics meaningfully influence how much they spend and how their spending patterns shift under different conditions.

The model examines several key questions. How does a customer's age affect their spending score when it increases by one year. Does a customer's work experience contribute to meaningful differences in purchasing behavior. Does family size shape how people allocate their shopping budget. By analyzing variables such as age, profession, income level, family size, and other demographic attributes, the model determines which factors significantly increase or decrease expected spending.

This analysis supports data-driven marketing strategies by revealing how individual characteristics translate into observable spending patterns.