ML based data analytics for IoT or WSN datasets from Kaggle or UCI data repository- need to compare the results with minimum 5 ML techniques
https://archive.ics.uci.edu/ml/datasets/Air+Quality
This dataset is from the UCI machine learning repository and contains hourly averaged responses from an air quality multi-sensor device that was located in a significantly polluted area at road level in an undisclosed Italian city. This data was collected over the course of approx one year (from March 2004 - February 2005)
- 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
- Data Preprocessing
- Plots
- Scatter Plots
- Line Plots
- Scatter Plots
- Violin Plot
- Correlation Plot
- Decision Tree Regression
- Random forest regression
- Linear Regression
- Linear Model
- Random Forests for Regression Analysis