Customer Segmentation with Machine Learning

📌 Project Overview

This repository contains machine learning models for customer segmentation using the Customer Personality Analysis dataset from Kaggle. The project explores clustering and classification techniques to segment customers based on their purchasing behavior and demographic attributes.

📊 Dataset

• Source: Customer Personality Analysis
• Attributes: Customer demographics, purchase history, and engagement metrics.
• Goal: Identify customer segments to enhance marketing strategies.

🛠️ Technologies Used

• Python
• Google Colab
• Scikit-learn
• Pandas & NumPy
• Matplotlib & Seaborn

🏗️ Methods Implemented

1. Data Preprocessing: Handling missing values, encoding categorical variables, feature scaling.
2. Exploratory Data Analysis (EDA): Understanding the dataset through visualizations.
3. Clustering: K-Means, DBSCAN, Hierarchical Clustering.
4. Classification: Decision Trees, Random Forest, SVM.
5. Model Evaluation: Silhouette Score, Confusion Matrix, Accuracy.

📌 Notebooks

• Notebook 1 - Clustering techniques.
• Notebook 2 - Classification models.

🚀 How to Use

1. Clone the repository:

   git clone https://github.com/yourusername/CustomerSegmentation-ML.git