README.md

metadata

license: mit
task_categories:
  - tabular-regression
tags:
  - motorsport
  - formula-racing
  - lap-time
  - tabular
  - gdgc-datathon
language:
  - en
size_categories:
  - 100K<n<1M

GDGC Datathon 2025 - Formula Racing Lap Time Dataset

Dataset for predicting Formula racing lap times, used in the GDGC Datathon 2025 competition.

Dataset Description

This dataset contains historical Formula racing data with various features related to circuits, weather conditions, rider/driver performance, and race configurations. The goal is to predict Lap_Time_Seconds.

Dataset Summary

Split	Samples	Size
Train	734,002	124 MB
Test	195,001	51 MB

Dataset Structure

Files

data/
├── train.csv    # Training data with target variable
└── test.csv     # Test data for predictions

Features

Column	Description	Type
id	Unique identifier	int
Unique ID	Alternative unique ID	int
Rider_ID	Rider/driver identifier	int
Formula_category_x	Racing formula category	categorical
Len_Circuit_inkm	Circuit length in kilometers	float
Laps	Number of laps in the race	int
Start_Position	Starting grid position	int
Formula_Avg_Speed_kmh	Average speed in km/h	float
Formula_Track_Condition	Track condition rating	categorical
Humidity_%	Humidity percentage	float
Tire_Compound	Type of tire compound used	categorical
Penalty	Penalty time/status	float
Champ_Points	Championship points	float
Champ_Position	Championship standing position	int
Session	Race session type	categorical
race_year	Year of the race	int
seq	Sequence number	int
position	Final position	int
points	Points earned	float
Formula_shortname	Short name of formula	categorical
circuit_name	Name of the circuit	categorical
Corners_in_Lap	Number of corners per lap	int
Tire_Degradation_Factor_per_Lap	Tire degradation rate	float
Pit_Stop_Duration_Seconds	Pit stop time in seconds	float
Ambient_Temperature_Celsius	Air temperature	float
Track_Temperature_Celsius	Track surface temperature	float
weather	Weather condition	categorical
track	Track identifier	categorical
air	Air condition metric	float
ground	Ground condition metric	float
starts	Number of race starts	int
finishes	Number of race finishes	int
with_points	Races finished with points	int
podiums	Number of podium finishes	int
wins	Number of wins	int
Lap_Time_Seconds	Target variable - Lap time in seconds	float

Target Variable Statistics

Metric	Value
Count	734,002
Mean	89.997 s
Std	11.532 s
Min	70.001 s
25%	79.989 s
50% (Median)	89.970 s
75%	99.914 s
Max	109.999 s

The target distribution is nearly symmetric with mean ≈ median, indicating no significant skew.

Usage

Loading with Pandas

import pandas as pd

# Load training data
train_df = pd.read_csv("train.csv")
print(f"Training samples: {len(train_df)}")

# Load test data
test_df = pd.read_csv("test.csv")
print(f"Test samples: {len(test_df)}")

# Separate features and target
X = train_df.drop(columns=["Lap_Time_Seconds", "id"])
y = train_df["Lap_Time_Seconds"]

Loading from Hugging Face

from huggingface_hub import hf_hub_download
import pandas as pd

# Download files
train_path = hf_hub_download(
    repo_id="Haxxsh/gdgc-datathon-data",
    filename="train.csv",
    repo_type="dataset"
)

test_path = hf_hub_download(
    repo_id="Haxxsh/gdgc-datathon-data",
    filename="test.csv",
    repo_type="dataset"
)

# Load into pandas
train_df = pd.read_csv(train_path)
test_df = pd.read_csv(test_path)

With Datasets Library

from datasets import load_dataset

dataset = load_dataset("Haxxsh/gdgc-datathon-data")

Trained Models

Pre-trained models for this dataset are available at:

• Models: Haxxsh/gdgc-datathon-models
• Training Code: ezylopx5/DATATHON

Evaluation Metric

The primary evaluation metric is RMSE (Root Mean Squared Error):

from sklearn.metrics import mean_squared_error
import numpy as np

rmse = np.sqrt(mean_squared_error(y_true, y_pred))

Data Preprocessing Tips

1. Handle categorical features: Use label encoding or one-hot encoding for columns like weather, circuit_name, Tire_Compound
2. Feature scaling: Normalize numerical features for certain models
3. Missing values: Check for and handle any missing values appropriately
4. Feature engineering: Consider creating interaction features or aggregations

License

MIT License

Citation

@dataset{gdgc-datathon-2025-data,
  author = {Haxxsh},
  title = {GDGC Datathon 2025 - Formula Racing Lap Time Dataset},
  year = {2025},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/Haxxsh/gdgc-datathon-data}
}

Acknowledgments

• GDGC Datathon 2025 organizers
• Formula racing data providers