This project explores and analyzes comprehensive Formula 1 datasets to uncover patterns, trends, and insights about races, drivers, constructors, circuits, and car performance over the years — with a focus on the hybrid era (2014–present).
The data comes from the Ergast Developer API F1 dataset which includes:
- Race results
- Driver standings
- Constructor results
- Circuit information
- Lap times, pit stops, qualifying sessions
- Status and reliability logs
- 🔍 Perform structured Exploratory Data Analysis (EDA) on F1 historical data.
- 🗺️ Analyze circuits by number of races hosted and how lap times evolved over the years.
- 🏁 Identify the most reliable constructors in the hybrid era (2014–present) based on DNF (Did Not Finish) rates.
- 📈 Visualize trends using bar plots and line charts to make results intuitive and presentable.
- Top 20 Circuits: Highlighted the most iconic circuits in F1 history by the number of races hosted.
- Lap Time Trends: Showed that lap times have generally improved year over year, reflecting advancements in car engineering.
- Constructor Reliability: Measured by lowest DNF rates; Mercedes stood out for consistent reliability in the hybrid era.
- Python (Pandas, Matplotlib, Seaborn)
- Jupyter Notebook / Google Colab
- Kaggle Dataset
- Data Merging, Cleaning, and Visualization