{"id":23938042,"url":"https://github.com/kanika300393/customer_segmentation","last_synced_at":"2026-06-11T22:31:51.963Z","repository":{"id":269764406,"uuid":"908391575","full_name":"Kanika300393/Customer_Segmentation","owner":"Kanika300393","description":"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. 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The objective is to identify distinct customer groups that can be targeted with personalized marketing strategies.\n\n## Project Overview\nThe 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.\n\nTo represent your workflow as a clean and organized table or box-based graph in your README file, you can include the following formatted table:\n\n---\n\n### Workflow\n\n| **Step**                          | **Description**                                                                                                                                          |\n|------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------|\n| **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.                |\n| **Feature Selection**              | Selected two features: **Annual Income** and **Spending Score**, to cluster the customers.                                                             |\n| **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. |\n| **K-Means Clustering**             | Applied K-Means clustering with **5 clusters**, and obtained labels for each customer.                                                                  |\n\n---\n\n\n## Visualization\nPlotted the customer clusters with different colors and highlighted the centroids to show how customers are grouped based on income and spending scores.\n\n\n\n![Heatmap](https://github.com/user-attachments/assets/f30d3516-2faf-4d8b-80d4-e1be7831d287)\n\n\n\n![Elbow_Chart](https://github.com/user-attachments/assets/b349b557-d2ec-48b1-bda4-aa05224df590)\n\n\n\n![Customer_Groups](https://github.com/user-attachments/assets/d460fcf0-02b1-4a20-bff1-d4b60c226988)\n\n\n\n![Centroid and Customer Segmentation](https://github.com/user-attachments/assets/99eedf9e-cafe-4e39-8892-3956e0456b99)\n\n\n\n![3D chart](https://github.com/user-attachments/assets/eb38e454-a89b-40bb-8214-b86a399bdc5e)\n\n\n\n![Pair_Plot](https://github.com/user-attachments/assets/47407a33-3bc2-4d2f-95ce-797feef97c1f)\n\n\n\n![Distribution of Annual Income](https://github.com/user-attachments/assets/d4b7984f-d107-4fad-8813-0b07445f83b0)\n\n\n\n![Distribution of Spending Score](https://github.com/user-attachments/assets/c1059775-0b68-4fb4-975d-4997c3790bee)\n\n\n## Results\nThe model successfully segmented the customers into 5 distinct groups based on their annual income and spending score, enabling targeted strategies for each group.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkanika300393%2Fcustomer_segmentation","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkanika300393%2Fcustomer_segmentation","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkanika300393%2Fcustomer_segmentation/lists"}