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README.md

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+---
+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
+
+```python
+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
+
+```python
+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
+
+```python
+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](https://huggingface.co/Haxxsh/gdgc-datathon-models)
+- **Training Code:** [ezylopx5/DATATHON](https://github.com/ezylopx5/DATATHON)
+
+## Evaluation Metric
+
+The primary evaluation metric is **RMSE** (Root Mean Squared Error):
+
+```python
+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
+
+```bibtex
+@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