F1 Hub - Comprehensive Formula 1 Database
A Pokédex-style Python application containing detailed Formula 1 information with statistical analysis capabilities.
Project Overview
F1 Hub is a comprehensive Formula 1 database application that provides detailed information about drivers, teams, circuits, and race statistics in an intuitive interface inspired by the Pokédex. The application serves as both an information resource and an analytical tool for F1 enthusiasts.
Key Features
- Extensive Database: Contains information on all F1 drivers (1950-present), teams, circuits, and race results
- Statistical Analysis: Compare driver/team performance across seasons and circuits
- Interactive Visualizations: Generate charts and graphs of performance metrics
- Predictive Modeling: Machine learning models to predict race outcomes based on historical data
- Live Data Integration: Option to fetch real-time race data during events
Data Collection
The project involved significant data collection and processing:
- Web Scraping: Developed custom scrapers for official F1 sites and fan wikis
- API Integration: Connected to Ergast Developer API for historical data
- Data Cleaning: Processed and normalized inconsistent historical records
- Database Design: Created optimized relational database schema
Technical Implementation
Data Layer
SQLite database with 20+ tables containing 100,000+ records
Analysis
Pandas for data manipulation and statistical analysis
Interface
Tkinter GUI with custom widgets and themes
ML Models
Scikit-learn for race outcome prediction
User Experience
The application was designed with both casual fans and hardcore enthusiasts in mind:
- Quick Search: Find any driver/team/circuit with autocomplete
- Comparison Tools: Side-by-side stats for any two entities
- Timeline View: Visualize performance changes over time
- Custom Reports: Generate printable summaries