🧍‍♂️🧍‍♀️ Customer Segmentation in Python

Prodigy Infotech Machine Learning Task 2

This project performs customer segmentation using unsupervised machine learning techniques on a mall customer dataset. It aims to group customers based on key features such as income and spending behavior to help businesses target specific customer segments more effectively.

Problem Statement

Create a K-means clustering algorithm to group customers of a retail store based on their purchase history.

📊 Dataset

Source: Kaggle - Customer Segmentation Tutorial in Python

The dataset contains information on 200 mall customers, including:

• CustomerID
• Gender
• Age
• Annual Income (k$)
• Spending Score (1–100)

🚀 How to Run

1. Clone the repository:

git clone https://github.com/SafalNarsingh/Prodigy_ML_02

2. Install dependencies:

  pip install numpy pandas matplotlib seaborn scikit-learn 

3. Launch jupyternotebook:

jupyter notebook customers.ipynb

📦 Requirements

• pandas
• matplotlib
• seaborn
• scikit-learn

📁 Files

.
├── data_file/
│   └── Mall_Customers.csv
├── customer.ipynb
└── readme.md'

🔧 Features

• Visual analysis using histograms, boxplots, and pairplots
• Encoding of categorical variables (Gender)
• Data scaling using StandardScaler
• Optimal number of clusters determined using the Elbow Method
• Clustering using:
• K-Means algorithm
• 2D/3D cluster visualization for intuitive interpretation