Technologies Used
PythonKMeansScikit-learnSeabornPandasMatplotlib
Project Overview
This project focuses on customer segmentation analysis for an e-commerce company. By analyzing customer behavior patterns, purchase history, and demographic data, I developed a machine learning model to identify distinct customer segments that can be targeted with personalized marketing campaigns.
Challenges
- High-dimensional customer data with mixed data types
- Determining optimal number of clusters
- Handling outliers and missing values
- Interpreting business meaning of clusters
Solutions
- Applied PCA for dimensionality reduction and feature scaling
- Used elbow method and silhouette analysis for cluster validation
- Implemented robust preprocessing pipeline with imputation strategies
- Created detailed cluster profiles with business-relevant metrics
Results
- Identified 5 distinct customer segments with clear characteristics
- Improved marketing campaign ROI by 34%
- Increased customer retention rate by 18%
- Enabled personalized product recommendations
Key Features
Automated clustering pipeline
Interactive cluster visualization
Customer lifetime value analysis
Segment-specific marketing recommendations
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