https://github.com/athari22/customer-segmentation

Customer Segmentation using Machine Learning
https://github.com/athari22/customer-segmentation

data-visualization kmeans kmeans-clustering machine-learning ml pca seaborn

Last synced: about 5 hours ago
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Customer Segmentation using Machine Learning

• Host: GitHub
• URL: https://github.com/athari22/customer-segmentation
• Owner: Athari22
• Created: 2022-12-01T17:18:57.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2022-12-01T17:51:39.000Z (almost 2 years ago)
• Last Synced: 2023-12-10T10:25:21.446Z (11 months ago)
• Topics: data-visualization, kmeans, kmeans-clustering, machine-learning, ml, pca, seaborn
• Language: Jupyter Notebook
• Homepage:
• Size: 287 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Customer Segmentation using Machine Learning

## Description

![img](https://github.com/Athari22/Customer-Segmentation/blob/main/Customer%20Segmentation%20using%20Machine%20Learning/segmentation.png)

Let’s say, you decided to buy a t-shirt from a brand online. Have you ever thought that who else bought the same t-shirt? People, who have similar to you, right? Same age, same hobbies, same gender, etc. 

Today, many businesses are going online and therefore online marketing is essential to retain customers. However, considering all customers as equal and targeting them all with similar marketing strategies is not an efficient way, since it also annoys the customers by neglecting their individuality, so customer segmentation has become very popular and has also become a viable solution. So, we actually try to find and group customers based on common characteristics such as age, gender, living area, spending behavior, etc. So that we can market the customers effectively.

## Problems you want to find answers 

The goal of customer segmentation is to divide the company’s customers based on their demographic characteristics (age, gender, marital status) and their behavior characteristics (types of products ordered, annual income). It’s a better approach for customer segmentation to focus on behavioral aspects rather than demographic characteristics since they do not emphasize individuality of customers.

## Dataset

### Context

This [dataset](https://www.kaggle.com/datasets/vjchoudhary7/customer-segmentation-tutorial-in-python) is created only for the learning purpose of the customer segmentation concepts , also known as market basket analysis .

### Content

You are owing a supermarket mall and through membership cards , you have some basic data about your customers like Customer ID, age, gender, annual income and spending score.

Spending Score is something you assign to the customer based on your defined parameters like customer behavior and purchasing data.