This repository contains a customer segmentation project implemented in a Jupyter Notebook using Python. Customer segmentation is a crucial strategy for businesses aiming to understand their customer base better, enabling targeted marketing strategies and personalized customer experiences.
The goal of this project is to analyze a dataset containing various customer attributes and behavior and segment customers into distinct groups based on their similarities. By leveraging unsupervised machine learning techniques, specifically clustering algorithms, this project aims to identify different customer segments with similar characteristics.
Programming Language: Python
Libraries: Pandas, NumPy, scikit-learn, Matplotlib, Seaborn
Machine Learning Techniques: K-means clustering
Jupyter Notebook: Contains the main project implementation with comments, data preprocessing steps, exploratory data analysis, and the application of clustering algorithms for customer segmentation.
Dataset: The dataset used for this project, anonymized to maintain privacy, comprising various customer features such as demographics, purchasing behavior, and more.
Clone this repository to your local machine.
Open the Jupyter Notebook in an environment with Python and required libraries installed (consider using Anaconda or a virtual environment).
Execute the cells sequentially to understand the project workflow and insights gained from customer segmentation.