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Customer Segmentation and Analysis Project

This repository contains code and analysis for a customer segmentation project. The project aims to segment customers based on their attributes and behavior, and provide insights to help businesses make informed decisions.

Table of Contents

Introduction

Customer segmentation is a critical task for businesses to better understand their customers and tailor their strategies accordingly. This project focuses on using clustering techniques to group customers based on attributes such as income, recency, age, and spending. The insights gained from this segmentation can drive targeted marketing campaigns and strategic decisions.

Project Overview

  • Data Preprocessing: The initial steps involve loading the data, cleaning and transforming it, and creating relevant features for analysis.

  • Exploratory Data Analysis (EDA): EDA is conducted to understand the distribution of attributes, uncover patterns, and identify outliers.

  • Feature Scaling: Numerical features are scaled using the MinMaxScaler to bring them to a similar range, which is essential for certain clustering algorithms.

  • Customer Segmentation: Clustering algorithms are applied to group customers based on their attributes. The resulting clusters help define customer segments.

  • Cluster Analysis: Customer segments are analyzed in terms of their characteristics, such as income, spending, and recency. Insights are drawn to guide business strategies.

  • Visualization: Various visualizations, such as box plots, bar plots, and heatmaps, are used to represent the distribution of attributes, customer clusters, and insights.

Usage

  1. Ensure you have Python and required packages installed.

  2. Open a Jupyter Notebook or Python script to run the provided code files.

  3. Run the code sequentially to perform data preprocessing, analysis, and visualization.

Analysis

  • Customer Distribution: The distribution of customers across different clusters is analyzed, revealing the relative composition of each segment.

  • Cluster Characteristics: Each cluster's mean, median, max, and min values for attributes like income, recency, age, total purchases, spending, and total children are presented to provide a comprehensive understanding of the clusters.

  • Insights and Recommendations: Insights are drawn from the analysis to provide actionable recommendations for business strategies. These insights can guide decisions on customer retention, engagement, and marketing campaigns.

Conclusion

The customer segmentation and analysis project provides a systematic approach to understand customer behavior and preferences. By leveraging clustering techniques, businesses can target different customer segments with tailored strategies, ultimately enhancing customer satisfaction and revenue.

Contributing

Contributions to this project are welcome! If you have any suggestions, improvements, or bug fixes, feel free to open an issue or create a pull request.