Customer Segmentation via Clustering 🛒📊

This project demonstrates customer segmentation using various clustering techniques. We explore and preprocess the data, reduce dimensionality, apply clustering algorithms, and interpret the results to gain insights about customer behavior.

Project Overview

In this project, we practice the following concepts:

1. Exploratory Data Analysis (EDA) 🔍
2. Data Preprocessing 🛠️
3. Feature Engineering 🪄
4. Dimensionality Reduction via PCA 📉
5. Clustering with KMeans, Agglomerative Clustering, and DBSCAN 📊
6. Hyperparameter Optimization with Optuna 🤖
7. Analysis of the clusters 🔬
8. Final Interpretation of the results 📝

Note: This project focuses on unsupervised learning for gaining insights rather than building predictive models.

This project helps in understanding the customer base by segmenting them into different groups based on their behavior, allowing for targeted marketing strategies.

View this notebook in nbviewer here.
Alternatively, if the above link is not working properly, please click on the .ipynb in the repository files.

PS. The dataset used in this project was sourced from Kaggle, you can find it here.