This project aims to perform customer segmentation on a Mall customer dataset using the K-Means clustering algorithm. The dataset contains information about customers such as their age, gender, annual income, and spending score. The goal of this project is to cluster the customers based on their purchasing behavior and demographic characteristics.
By employing the K-Means clustering technique, we have effectively partitioned the data into various subgroups, predicated upon a diversity of distinct features. Subsequently, the management of the mall is able to effectively concentrate their efforts on particular clusters that possess an average spending score, with the objective of maximising the overall profitability.
In addition, it is of paramount importance to cultivate and nurture a strong rapport with those select, premium patrons that exhibit elevated spending scores. Finally, it is incumbent upon the mall's management to proactively engage in ideation and innovation in order to devise novel methodologies for the purpose of stimulating and augmenting the purchasing proclivities of those patrons who exhibit relatively low spending scores.
The dataset used in this project is mall-customers-data.csv. It contains 200 rows and 5 columns:
- customer_id: unique ID assigned to the customer.
- gender: gender of the customer (male or female).
- age: age of the customer.
- annual_income: annual income of the customer in thousands of dollars.
- spending_score: score assigned by the mall based on customer behavior and spending nature.