F1 Data Visualization 🏎️

A comprehensive data visualization tool built with Python to analyze Formula 1 telemetry and real-time data. This application provides insights into race performance, lap times, and various other metrics through an interactive GUI interface.

🌟 Features

• Real-time telemetry data visualization

• Multiple analysis types:

  • Lap Time Analysis
  • Fastest Lap Comparison
  • Fastest Sectors Analysis
  • Full Telemetry Visualization
  • Tyre Compound and Stint Analysis
  • Pit Stop Impact Analysis
  • Fuel Usage Impact Analysis
  • Position Changes Tracking

• Machine Learning predictions for pit stop strategies

• Interactive GUI with customizable parameters

• Support for historical race data analysis

• Multi-driver comparison capabilities

🔧 Technologies Used

• Python 3.12.0
• PyQt5 - GUI Framework
• FastF1 - F1 Data Access
• Pandas - Data Manipulation
• Matplotlib/Seaborn - Data Visualization
• Scikit-learn - Machine Learning
• Apache Spark - Big Data Processing
• Dash - Web-based Visualization
• HDFS - Data Storage

📋 Prerequisites

• Python 3.8 or higher
• Apache Spark installation
• HDFS setup (for big data storage)

🚀 Installation

1. Clone the repository.

2. Install required packages:

   pip install -r requirements.txt

3. Configure HDFS connection:

   • Update the HDFS cluster IP in spark.py
   • Ensure proper permissions for data access

💻 Usage

GUI Application

Run the main GUI application:

python gui.py

Model Prediction Window

For standalone model predictions:

python gui_model.py

Web Dashboard

Launch the Dash web interface:

python app.py

📊 Available Analysis Types

1. Lap Time Analysis

   • Compare lap times between drivers
   • Track performance evolution

2. Fastest Lap Analysis

   • Identify and analyze fastest laps
   • Sector-by-sector breakdown

3. Full Telemetry

   • Speed traces
   • Throttle/brake patterns
   • Gear usage analysis

4. Strategy Analysis

   • Tyre compound impact
   • Pit stop timing
   • Fuel load effects

🔄 Data Pipeline

1. Data Collection (fastf1)

   • Live timing data
   • Historical race data
   • Driver telemetry

2. Processing (Spark)

   • Data cleaning
   • Feature engineering
   • Performance calculations

3. Storage (HDFS)

   • Raw data storage
   • Processed datasets
   • Model artifacts

4. Analysis

   • Statistical analysis
   • Machine learning predictions
   • Performance comparisons

🤖 Machine Learning Features

• Pit stop prediction model
• Performance trend analysis
• Anomaly detection
• Strategy optimization