This work demonstrates the application of spline interpolation on real-world data to estimate the missing values of temperature and humidity throughout a day. By using the CubicSpline function from SciPy's interpolate module, we can create a smoother transition between the data points and obtain a more realistic continuous curve that represents the data trends accurately.
The dataset consists of temperature and humidity measurements taken at specific hours throughout a single day. The temperature is measured in degrees Celsius, and humidity is given as a percentage. The data points are simulated for the purpose of this demonstration.
The data is interpolated using a cubic spline, which is a type of spline built of piecewise third-order polynomials. This method is particularly well-suited for smoothly connecting data points without assuming a single global model, which makes it flexible and adaptive to local variations in the data.
Temperature_Humidity_Interpolation.ipynb: Jupyter Notebook containing the interpolation analysis.
The interpolation results are visualized in two graphs. One graph displays the temperature against the hours of the day, and the other displays the humidity against the hours of the day. The blue dots represent the original data points, and the orange line represents the interpolated values, showing the temperature increase until the mid-afternoon and decrease towards the evening, and the inverse pattern for humidity.
To run this project, you will need Python and the following libraries:
- NumPy
- SciPy
- Matplotlib
To execute the analysis, run the Temperature_Humidity_Interpolation.ipynb notebook in a Jupyter environment. The notebook includes comments and markdown cells that guide you through the process.
Contributions to this project are welcome. You can improve the current interpolation, add new datasets, or suggest a new feature. Please submit a pull request or open an issue if you have any feedback.
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