https://github.com/kanika300393/customer_segmentation

This project performs customer segmentation on a retail dataset using K-Means clustering. The dataset includes features like annual income and spending score, which are used to group customers into 5 distinct segments. The Elbow method is applied to determine the optimal number of clusters, and visualizations are generated to represent the groups.
https://github.com/kanika300393/customer_segmentation

Last synced: 5 months ago
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This project performs customer segmentation on a retail dataset using K-Means clustering. The dataset includes features like annual income and spending score, which are used to group customers into 5 distinct segments. The Elbow method is applied to determine the optimal number of clusters, and visualizations are generated to represent the groups.

• Host: GitHub
• URL: https://github.com/kanika300393/customer_segmentation
• Owner: Kanika300393
• Created: 2024-12-26T00:43:25.000Z (7 months ago)
• Default Branch: main
• Last Pushed: 2024-12-26T01:04:22.000Z (7 months ago)
• Last Synced: 2025-01-06T02:18:53.920Z (6 months ago)
• Language: Jupyter Notebook
• Homepage:
• Size: 552 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Customer Segmentation

![image](https://github.com/user-attachments/assets/2b23605a-dace-415d-bae9-ccf4e6d81d3c)

This project focuses on segmenting customers based on their annual income and spending score using K-Means Clustering. The objective is to identify distinct customer groups that can be targeted with personalized marketing strategies.

## Project Overview

The project involves analyzing a dataset of mall customers to cluster them into groups based on their annual income and spending score. The K-Means algorithm is used for unsupervised learning, with the number of clusters determined using the Elbow Method.

To represent your workflow as a clean and organized table or box-based graph in your README file, you can include the following formatted table:

---

### Workflow

| **Step**                          | **Description**                                                                                                                                          |

|------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------|

| **Data Collection and Analysis**   | Loaded the dataset from `Mall_Customers.csv` and explored its structure. Checked for missing values and summary statistics of the dataset.                |

| **Feature Selection**              | Selected two features: **Annual Income** and **Spending Score**, to cluster the customers.                                                             |

| **Optimal Number of Clusters**     | Used the Elbow Method to determine the optimal number of clusters. The number of clusters was found to be **5** based on the Within-Cluster Sum of Squares (WCSS) graph. |

| **K-Means Clustering**             | Applied K-Means clustering with **5 clusters**, and obtained labels for each customer.                                                                  |

---

## Visualization

Plotted the customer clusters with different colors and highlighted the centroids to show how customers are grouped based on income and spending scores.

![Heatmap](https://github.com/user-attachments/assets/f30d3516-2faf-4d8b-80d4-e1be7831d287)

![Elbow_Chart](https://github.com/user-attachments/assets/b349b557-d2ec-48b1-bda4-aa05224df590)

![Customer_Groups](https://github.com/user-attachments/assets/d460fcf0-02b1-4a20-bff1-d4b60c226988)

![Centroid and Customer Segmentation](https://github.com/user-attachments/assets/99eedf9e-cafe-4e39-8892-3956e0456b99)

![3D chart](https://github.com/user-attachments/assets/eb38e454-a89b-40bb-8214-b86a399bdc5e)

![Pair_Plot](https://github.com/user-attachments/assets/47407a33-3bc2-4d2f-95ce-797feef97c1f)

![Distribution of Annual Income](https://github.com/user-attachments/assets/d4b7984f-d107-4fad-8813-0b07445f83b0)

![Distribution of Spending Score](https://github.com/user-attachments/assets/c1059775-0b68-4fb4-975d-4997c3790bee)

## Results

The model successfully segmented the customers into 5 distinct groups based on their annual income and spending score, enabling targeted strategies for each group.