🛍️ Customer Segmentation using K-Means

👨‍💻 Author

Aqib Ahmed

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📌 Project Description

This project applies K-Means Clustering to segment customers of a retail store into distinct groups based on their Annual Income and Spending Score.

Customer segmentation is a key strategy in marketing and helps businesses:

• Identify different types of customers
• Create targeted marketing campaigns
• Design loyalty programs
• Personalize offers and discounts

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📂 Dataset

• Name: Customer Segmentation Tutorial in Python
• Source: Kaggle Dataset
• File Used: Mall_Customers.csv

Dataset Columns:

• CustomerID → Unique customer ID
• Gender → Male / Female
• Age → Age of the customer
• Annual Income (k$) → Annual income in thousand dollars
• Spending Score (1-100) → Spending score assigned by the mall

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⚙️ Steps Involved

1. Load the dataset using Pandas.
2. Select features → Annual Income & Spending Score.
3. Elbow Method → Determine optimal number of clusters (k).
4. Apply K-Means Clustering with chosen k.
5. Visualize clusters using scatter plots with centroids.
6. Interpret customer groups.

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📊 Visualizations

Elbow Method

Helps to choose the optimal number of clusters (k).

Customer Segments

Clusters customers into groups such as:

• High Income – High Spending (Luxury Shoppers 💎)
• Low Income – Low Spending (Budget Customers 💵)
• High Income – Low Spending (Careful Customers 🧐)
• Medium Income – Medium Spending (Average Customers 🙂)
• Low Income – High Spending (Impulsive Customers ⚡)

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🚀 Tech Stack

• Python
• Pandas → Data handling
• Matplotlib / Seaborn → Visualization
• Scikit-Learn → K-Means Clustering

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🏆 Results

The K-Means algorithm successfully divided customers into 5 meaningful groups, helping the retail store better understand customer behaviors and design targeted strategies.

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📌 How to Run

1. Clone this repository:

   git clone https://github.com/Aqibahmed12/PRODIGY_ML_2.git
   cd PRODIGY_ML_2

2. Install dependencies:

   pip install pandas matplotlib seaborn scikit-learn

3. Run the notebook:

   jupyter notebook Task-02_KMeans_Customer_Segmentation.ipynb