AI/MLPredictive Modeling
Customer Segmentation with KMeans Clustering
Interactive ML pipeline for customer behavior analysis
Customer segmentation using KMeans clustering in Python (Nov 2024 – Mar 2025). Includes data preprocessing, Silhouette evaluation, PCA visualization and an interactive Streamlit dashboard. Complete unsupervised machine learning pipeline for segmenting customers with real-time cluster exploration and marketing analytics insights.
Tech Stack
Pythonscikit-learnStreamlitPandasNumPyMatplotlibSeabornGit/GitHub
Technical Highlights
- Data preprocessing with StandardScaler and LabelEncoder for cleaning, encoding, and scaling customer data
- KMeans clustering with Silhouette Score evaluation for optimal cluster quality assessment
- PCA dimensionality reduction for 2D cluster visualization with Matplotlib and Seaborn
- Interactive Streamlit dashboard for real-time data exploration and dynamic cluster count adjustment
Challenges & Solutions
- Optimal cluster selection → Implemented Silhouette Score evaluation to measure cluster cohesion and separation
- High-dimensional data visualization → Applied PCA for dimensionality reduction to create interpretable 2D plots
Gallery