Update README.md

Author: alifrmf

README.md

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-# Customer-Segmentation-Using-Clustering-Algorithms
-Customer Segmentation Using Unsupervised Machine Learning Algorithms
+# 🛒💳Customer Segmentation ![license](https://img.shields.io/github/license/alifrmf/Country-Profiling-Using-PCA-and-Clustering.svg) ![releases](https://img.shields.io/github/release/alifrmf/Country-Profiling-Using-PCA-and-Clustering.svg)
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+**Customer Segmentation Using Unsupervised Machine Learning Algorithms**
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+![Customer Segmentation (1)](https://user-images.githubusercontent.com/105715834/233505669-1b249cf7-075e-4655-9cc9-944ce0b9cbd1.gif)
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+**☀️What is Customer Segmentation?**
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+Customer segmentation is the practice of categorizing customers into distinct groups based on shared characteristics, enabling companies to target and tailor their marketing strategies to each group effectively. Customers are typically segmented based on their similarities in behavior, preferences, and purchasing habits.
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+**☀️Introduction:** 
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+The main task of **Clustering** is to identify natural groups within an unlabeled dataset. This means that clustering is an **Unsupervised Machine Learning** task, which is important in many scientific, engineering, and business domains. Some well-known applications of clustering include:
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+- Customer segmentation for efficient marketing
+- Image segmentation for computer vision
+- Document clustering for information retrieval
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+**☀️Objective:**  
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+This project demonstrates how to perform customer segmentation for a shopping mall using machine learning algorithms. This is an unsupervised clustering problem, and we will present and compare five popular clustering algorithms: **K-Means Clustering, Hierarchical Clustering, Gaussian Mixture Clustering, Mini-batch K-Means Clustering**, and **DBSCAN Clustering**. The main goal of this notebook is to cover the basics of clustering methods while also touching on some more advanced aspects.
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+![White and Blue Simple Monthly Budget Pie Chart](https://user-images.githubusercontent.com/105715834/233505817-18d55dee-2a30-43b7-aa69-b1c3fa59a329.gif)
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