Analysis of User Behavior Patterns in DeFi (Decentralized Finance) Applications
This project aims to analyze and segment user behavior using advanced clustering techniques. The goal is to provide actionable insights to enhance user experience and optimize marketing strategies.
BLOCKCHAINDECENTRALIZED FINANCEGENERATIVE AILLMSTREAMLIT APPDOCKER
3/22/20251 min read
Main Features
Data Collection and Preprocessing: gathering, cleaning, and compiling datasets from various APIs.
Data Exploration: Using descriptive statistics and visualizations to understand trends and patterns.
Modeling: Applying various clustering algorithms (K-Means, DBSCAN, etc.) and fine-tuning to optimally segment users.
Evaluation and Interpretation: Validating and interpreting results to derive actionable insights.
Recommendation System: developing a personalized recommendation system using generative AI to analyze specific user behaviors and suggest improvements.
Dataset
Technology Stack
API: CoinGecko, Etherscan, Yahoo Finance for data extraction.
Database: MongoDB for data collection and organization.
Machine Learning: Scikit-learn for unsupervised learning algorithms and Optuna for fine-tuning.
Data Visualization: Matplotlib, Seaborn, and Plotly for traditional visualization, and DataShader for large-scale visualization (rasterization).
Text Generation: utilization of a LLaMA 3 model via Groq's API for low-latency text-to-text generation.
User Interface with Streamlit: intuitive and interactive interface to present the project.
CI/CD: testing and deployment with GitHub Actions and Docker.
Cloud Deployment: application deployment on the Hugging Face Hub.