The goal of the project is to go through the CRISP-DM cycle of a Machine Learning Project. For this project in particular, that means:
- Analyse the dataset
- Discover and present patterns that you find
- Train a regressor on humidity and minimize the mean squared error
- Use XAI tools to explain your classifier to stakeholders and debug it
The dataset is public from the UCI ML repository.
| Feature No. | Description |
|---|---|
| 0 | Date (DD/MM/YYYY) |
| 1 | Time (HH.MM.SS) |
| 2 | True hourly averaged concentration CO in mg/m^3 (reference analyzer) |
| 3 | PT08.S1 (tin oxide) hourly averaged sensor response (nominally CO targeted) |
| 4 | True hourly averaged overall Non Metanic HydroCarbons concentration in microg/m^3 (reference analyzer) |
| 5 | True hourly averaged Benzene concentration in microg/m^3 (reference analyzer) |
| 6 | PT08.S2 (titania) hourly averaged sensor response (nominally NMHC targeted) |
| 7 | True hourly averaged NOx concentration in ppb (reference analyzer) |
| 8 | PT08.S3 (tungsten oxide) hourly averaged sensor response (nominally NOx targeted) |
| 9 | True hourly averaged NO2 concentration in microg/m^3 (reference analyzer) |
| 10 | PT08.S4 (tungsten oxide) hourly averaged sensor response (nominally NO2 targeted) |
| 11 | PT08.S5 (indium oxide) hourly averaged sensor response (nominally O3 targeted) |
| 12 | Temperature in °C |
| 13 | Relative Humidity (%) |
| 14 | AH Absolute Humidity |
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