Retail Customer Clustering
5 customer segments
Segments retail customers by spending behaviour. K-means over 4,000+ records, with the elbow method for choosing k, settling on five groups. Built during my Prodigy Info Tech placement.
1 / 2Identified 5 customer segments
Processed 4,000+ records
Clear spending pattern analysis
SnapshotAt a glance
ML Engineer (Prodigy Info Tech)
1 month
Identified 5 customer segments
Processed 4,000+ records
Clear spending pattern analysis
Technologies Used
5 Technologies Integrated
Impact
Key Features
Feature Implementation
Project Vision
Identify distinct customer segments for targeted marketing.
Core Process
The process of Developing it.
Analyzed customer data, performed feature engineering, applied K-means clustering, and visualized segments.
Build notesWhat I built
03Implemented K-means clustering algorithm using Scikit-learn to analyze customer spending patterns, processing 4,000+ customer records and identifying 5 distinct customer segments, where K-means finds segments and spending patterns speak volumes about customer behavior.
Developed a feature engineering and normalization pipeline using Pandas and NumPy, applying the elbow method for optimal cluster selection and feature scaling, enabling accurate customer segmentation that reveals meaningful patterns in retail data.
Created detailed data visualizations using Matplotlib that illustrate customer segments, spending patterns, and cluster characteristics, building a system where 5 distinct groups emerge from thousands of records, each with its own story that helps businesses understand and target their customers effectively.
Helping businesses understand customer behavior through segmentation.
Features
- Customer segmentation
- Spending pattern analysis
- K-means clustering
- Data visualization
Challenges
- Optimal cluster number selection
- Feature scaling
- Interpreting clusters
