R-D-exercise

Setup Requirements for this Jupyter Notebook:

To properly run the contents of this notebook, please ensure that the following libraries are installed in your environment:

1. Pandas: For data manipulation and analysis.

   pip install pandas
   

2. NumPy: For numerical operations.

   pip install numpy
   

3. Plotly: For interactive graphing and visualization.

   pip install plotly
   

4. PyArrow: To enhance Pandas' performance with Arrow's columnar memory format.

   pip install pyarrow
   

5. Scikit-Learn (sklearn): For machine learning and data preprocessing.

   pip install scikit-learn
   

6. SciPy: For scientific and technical computing.

   pip install scipy
   

Please ensure that all of the above packages are installed in your running environment to fully execute the code and analyses presented in this notebook.

Assumptions and Limitations:

1. Assumptions:

   • Continuity of data might be expected under normal circumstances, but the dataset's limited nature has necessitated particular handling of machine 2's GPH value.
   • The models assume that the relationships observed between input variables and power usage are stable and generalize across similar contexts.

2. Limitations:

   • The restricted dataset may conceal nuanced behaviors of the machines, potentially impacting the accuracy of predictions.
   • The current objective function and constraints may not capture all relevant factors influencing the system's behavior.

Rationale:

The analytical approach and model choices reflect a carefully balanced consideration of the data's characteristics, the system's underlying mechanics, and the ultimate goal of power conservation. The specificity of constraints and the global optimization strategy underscores a tailored, data-driven methodology.

Notes for Future Enhancement:

A pathway for further refinement lies in the customization of a more effective cost function, designed to penalize unwanted results. Such an adaptation may offer a more nuanced control over the model, thereby enhancing the performance of the optimization process. By aligning the cost function more closely with the specific requirements and constraints of the system, future iterations of the model could yield even more efficient and targeted solutions.