⚡ Project Pokédex - Where Data Meets Pocket Monsters
Advanced ML and Data Visualization with GCP
Project Overview:
Innovative data science exploration applying advanced ML and visualization techniques to Pokémon datasets using cutting-edge GCP technologies
GCP Components:
├── Vertex AI for ML model training
├── Google Colab for development environment
├── Python ecosystem for data processing
├── Matplotlib for data visualization
└── Looker Studio for interactive dashboards
Methodology:
├── OSINT data collection from multiple sources
├── Data cleaning and classification
├── Exploratory data analysis (EDA)
├── ML model training with Vertex AI
└── Interactive visualization creation
Key Features:
├── Predictive type classifications
├── Pattern recognition and trend analysis
├── Interactive dashboards
├── Advanced data visualizations
├── Auto ML training pipelines
└── Statistical analysis of Pokémon attributes
Technical Challenges:
├── Handling diverse Pokémon attributes
├── Multi-class type prediction
├── Creating interactive visualization techniques
├── Model optimization and tuning
├── Feature engineering for better predictions
└── Dashboard performance optimization
Key Insights:
├── Comprehensive understanding of Pokémon characteristics
├── Data-driven type predictions
├── Visual exploration of complex datasets
├── Correlation patterns between attributes
└── Evolution patterns and trends
Technologies Used:
Vertex AI, Auto ML, Google Colab, Python, Matplotlib, Looker Studio, Machine Learning, Data Visualization, Pandas, NumPy, Scikit-learn
Architecture Diagram:
