This project applies unsupervised learning techniques to segment customers based on their purchasing behaviors. By clustering customers into meaningful groups, businesses can target their strategies more effectively and optimize resource allocation.
The main goal of this project is to identify distinct customer segments using clustering algorithms. Key steps include:
- Data preprocessing and feature scaling.
- Dimensionality reduction using Principal Component Analysis (PCA).
- Clustering with K-Means and analyzing the results.
- Python
- Scikit-learn for clustering and PCA
- Matplotlib and Seaborn for data visualization
- Identified unique customer segments based on their purchasing behaviors.
- Generated actionable insights for targeted marketing and resource optimization.
- Clone the repository:
git clone https://github.com/BaraSedih11/Identify_Customer_Segments.git
- Install the required libraries:
pip install -r requirements.txt- Run the notebook or script for analysis.
- here is the last dataset: dataset
This project is part of the Udacity Intro to Machine Learning with TensorFlow Nanodegree.