Data analysis, visualization and Model demonstration interface using Streamlit. It's a part of H-Beacon: Smart Irrigation System project. Enables easy and quick sensor data analysis, visualization and trained models inference for any newly acquired data without repeated coding. Application is deployed on Heroku.
H-Beacon is the deep sequential neural network model that estimates soil humidity from the strength of the LoRa-beacon IoT signal. We are funded by Horizon 2020 EU funding for Research & Innovation.
$ git clone https://github.com/TomislavZupanovic/H-Beacon-App.git$ streamlit run app.py-
Variables plot in respect to soil humidity
Plotting variables for chosen sensor for any time frame and moving average to see any occuring patterns in data.
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Correlation matrix
Descriptive statistics and correlations matrix for any chosen time frame.
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Scatter plot
Scatter plot between any two variables with dropdown menu for choosing.
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Data transformations and analysis
Data can be easily transformed (Logarithm, Squared etc.) to check for distributions with histograms, applying operations to analize stationarity and standard deviations.
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Model inference
Choosing trained models to estimate soil humidity on any time frame, showing metrics, residual and error plots for model performance.
