Objective: Utilize k$-$means clustering to segment customers of a mall based on their spending behavior, aiming to provide personalized services and improve marketing strategies.

Dataset: Use the "Mall Customer Segmentation Data" available on the UCI Machine Learning Repository. This dataset contains information about customers, including their age, annual income, and spending score.

Dataset Link: Mall Customer Segmentation Data

Tasks:

$$ Data Exploration:

$$ Load and explore the Mall Customer Segmentation dataset.

$$ Check for missing values, outliers, and data distributions.

$$ K$-$means Clustering:

$$ Apply k$-$means clustering to segment customers based on features like annual income and spending score.

$$ Experiment with different values of k and choose the optimal number of clusters.

$$ Visualization:

$$ Visualize the results of the clustering, perhaps using a scatter plot to display clusters based on income and spending score.

$$ Explore additional visualizations to understand the characteristics of each cluster.

$$ Cluster Profiling:

$$ Analyze and profile each cluster to understand the common characteristics of customers within each segment.

$$ Provide insights on spending patterns, demographics, and potential marketing strategies for each cluster.

$$ Validation:

$$ Validate the clustering results using internal validation metrics and explore any patterns or trends that emerge.

$$ Interactive Web Application $($Optional$)$:

$$ Create a simple interactive web application using tools like Dash or Streamlit to visualize the customer segments dynamically.

Deliverables:

$$ Jupyter Notebook or Python script containing the code.

$$ Visualizations showcasing data exploration, clustering results, and customer segment profiles.

$$ A report summarizing the findings, insights, and potential actionable strategies for the mall.