The Colab Notebook link of this project: https://colab.research.google.com/drive/1W15eOQbeHp9wbJWRaE0f7ZfYv_jAj14O?usp=sharing
View this project on nbviewer (if you face any problem opening Jupyter Notebook): https://nbviewer.jupyter.org/github/Abrar2652/Road-Friction-Forecasting/blob/master/Road_Friction_Forecasting.ipynb
The dataset has been collected from the Smart Road - Winter Road Maintenance Challenge 2021 organized by UiT The Arctic University of Norway on Devpost. Hackathon link: https://dit4bears.devpost.com
Dataset download link: https://uitno.app.box.com/s/bch09z27weq0wpcv8dbbc18sxz6cycjt
After downloading the smart_road_measurements.csv file from the competition page, we had added extra columns collecting data from the external resources authorized the organizers. The links of the external datasets are:
[1] Weather data https://pypi.org/project/wwo-hist/
[2] UV Index data https://pyowm.readthedocs.io/en/latest/v3/uv-api-usage-examples.html
- Python
- Jupyter Notebook
- Microsoft Excel
- Pandas
- XGBooster regressor
- Matplotlib
- Seaborn heatmap
- Optuna