Mall Customers Clustering Analysis

Overview

This project involves analyzing customer data from a mall dataset to understand customer segments. The analysis includes visualizing data distributions and applying K-means clustering. This document provides an overview of the technologies and modules used in the project.

Technologies and Modules Used

• Pandas: A powerful data manipulation and analysis library for Python. Used for loading and handling the dataset.

• NumPy: A library for numerical computing in Python. Provides support for arrays and mathematical functions.

• Seaborn: A statistical data visualization library based on Matplotlib. Used for creating distribution plots and density plots.

• Matplotlib: A comprehensive library for creating static, animated, and interactive visualizations in Python. Used for plotting the data distributions.

• Scikit-Learn: A machine learning library for Python that includes tools for clustering and other machine learning tasks. Used for applying K-means clustering to identify customer segments.

• Warnings: A module for managing warnings in Python. Used to suppress warnings that may arise during execution.

Features

• Data Exploration: Analyze and visualize the distribution of customer attributes such as age, annual income, and spending score.

• Density Visualization: Compare income distributions across different customer genders using density plots.

• Clustering Analysis: Apply K-means clustering to determine optimal customer segments based on annual income.

Installation

To replicate this analysis, ensure you have Python installed and use pip to install the necessary libraries:

pip install pandas numpy seaborn matplotlib scikit-learn