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-# Customer-Segmentation-using-Machine-Learning
+# 🛍️ Customer Segmentation using Machine Learning
 
-The retailer has hired us to help them create customer clusters, a.k.a "customer segments" through a data-driven approach.
+Welcome to the **Customer Segmentation** project!
 
-They've provided us a dataset of past purchase data at the transaction level.
-Our task is to build a clustering model using that dataset.
-Our clustering model should factor in both aggregate sales patterns and specific items purchased.
+In this project, we use **unsupervised machine learning** techniques to divide customers into different groups (called **clusters**) based on their purchasing behavior.
 
-This project is based on Unsupervised Learning.
+The idea is to help a retailer better understand their customers — for example, who are the high spenders, which customers buy similar items, or who buys frequently but spends less.
 
-Input dataset is present in Files folder.
+---
 
-I jupyter Notebbook their is actual coding right from Data Analysis to clustering using K-mean.
+## 📂 What This Project Does
+
+The retailer has provided **transaction-level data** (i.e., each row is a customer's purchase).
+
+Our job is to:
+
+* Analyze this data
+* Understand buying patterns
+* Build a **clustering model** that groups similar customers together
+
+We use:
+
+* **Data analysis** to explore the data
+* **K-Means clustering** to create the customer groups
+
+---
+
+## 🧠 What is Unsupervised Learning?
+
+This project uses **unsupervised learning**, which means:
+
+* We don’t have any labels (like "loyal customer" or "one-time buyer")
+* The model finds patterns and forms groups **on its own**, based only on the data
+
+---
+
+## 📊 What’s in the Notebook?
+
+The Jupyter Notebook included in this project walks through the entire process step-by-step:
+
+1. **Loading the data**
+2. **Exploring and cleaning the data**
+3. **Analyzing customer behavior**
+4. **Building a clustering model (K-Means)**
+5. **Visualizing the customer segments**
+6. **Interpreting the results**
+
+---
+
+## 📁 Dataset
+
+The input dataset is located in the **Files** folder.
+
+It contains:
+
+* Customer IDs
+* Items purchased
+* Quantity
+* Price
+* Transaction date
+* And more...
+
+---
+
+## 🔧 Tools & Libraries Used
+
+* Python
+* Jupyter Notebook
+* Pandas
+* NumPy
+* Matplotlib / Seaborn
+* Scikit-learn (for K-Means)
+
+---
+
+## 📈 Outcome
+
+At the end of the project, you'll be able to:
+
+* Understand how to segment customers using data
+* Create meaningful clusters
+* Use clustering results to improve business decisions (like marketing or product targeting)
+
+---
+
+## 🚀 Getting Started
+
+To run this project on your machine:
+
+1. Clone the repo or download the files
+2. Open the Jupyter Notebook
+3. Run the cells step-by-step
+
+---
+
+## 💡 Who is this for?
+
+This project is great for:
+
+* Beginners in machine learning
+* Anyone interested in customer analytics
+* Aspiring data analysts or data scientists
+
+---
+
+## 📬 Questions?
+
+If you have any questions, feel free to reach out or open an issue.