Customer Segmentation using K-Means Clustering

Welcome to the "Customer Segmentation using K-Means Clustering" GitHub repository. This project focuses on leveraging the K-Means clustering algorithm to perform customer segmentation based on key variables - annual income and spending score. The outcome of this endeavor is to derive actionable insights for targeted marketing and business strategy refinement.

Introduction

In the realm of marketing and business analytics, customer segmentation is an instrumental practice. It involves grouping customers who share similar attributes to enhance comprehension of their behaviors and preferences. This project embarks on customer segmentation by employing K-Means clustering, a data-driven technique that partitions customers into distinct groups based on their annual income and spending score.

Getting Started

To commence with this project, ensure that the requisite libraries are installed by executing the initial code block in the Importing the libraries and the data section. Proceed with the following steps:

Importing the Data

1. Make certain that a CSV file named 'Customers.csv', containing customer data, is accessible.
2. Import the data using the provided code:

   import pandas as pd
   data = pd.read_csv('Customers.csv')

Data Exploration

Delve into the dataset by leveraging functions such as data.head(), data.tail(), data.shape, data.info(), and data.describe(). These operations offer profound insights into the data's structure and essential statistical summaries.

Data Preprocessing

Preprocess the data by undertaking actions like column renaming, missing value management, and label refinement as delineated in the Data Preprocessing section.

Determining Optimal Clusters

Employ the Elbow Method to ascertain the optimal number of clusters. Compute the Within-Cluster Sum of Squares (WCSS) for varying k (number of clusters) values and construct a WCSS-k plot. The "elbow point," where the WCSS rate of decrease decelerates, signifies an appropriate cluster count.

K-Means Clustering

Execute the K-Means clustering procedure utilizing the optimal cluster count derived from the Elbow Method. Instantiate a KMeans object with the designated cluster count, fit the model to the data, and predict cluster assignments for each data point.

Visualization

Elicit meaningful insights through scatterplots and boxplots, as detailed in the **Visualization **segment. These visual representations, harnessed via libraries like Matplotlib and Seaborn, illuminate the inherent structures within the data.

Analysis

Conduct an in-depth analysis of the clusters, elucidating their implications based on the visualizations. Expound upon the distinctive attributes of each cluster and elucidate their significance in the context of customer behavior and preferences.

Conclusion

This project epitomizes the process of customer segmentation via K-Means clustering. The derived clusters furnish businesses with profound insights that underpin targeted marketing initiatives and strategic decision-making. The provided code snippets and visualizations furnish a solid groundwork for extended analysis and informed decision-making.

Feel free to navigate and adapt the code to align with your specific dataset and business objectives. Best of luck in your customer segmentation journey!