Project Name: Mall Customer Segmentation

Objective: The goal of this project is to divide the mall's target market into approachable groups, creating subsets of customers based on demographics and behavior. This will help to better understand the target audience for marketing activities.

Problem Statement: The objective is to help the marketing team understand the target customers. This will allow them to plan better marketing strategies by identifying key customer segments based on income, age, and shopping score. Your boss wants you to identify the most important shopping groups and determine the ideal number of clusters with labels for each group.

Context: You have been asked to segment mall customers into distinct groups based on shopping data. The primary factors considered are income, age, and shopping score. The ideal number of groups (clusters) should be identified, each with a clear label.

Analysis:

Cluster 1: This group consists of high-income individuals with high spending scores. 54% of these customers are women. Marketing campaigns should focus on popular items that appeal to this segment to increase engagement. Cluster 2: This group represents an interesting opportunity for sales events. Customers in this cluster can be targeted for discounts on popular items. Tools Used:

Pandas: For data manipulation and analysis. Matplotlib: For data visualization. Seaborn: For creating attractive and informative statistical graphics. Scikit-learn: For performing clustering using machine learning techniques.