Thermal Conductivity Data: Data correlating temperature, pressure, and

thermal conductivity in various materials

Tasks:

Data Exploration: Familiarize yourself with the selected dataset. Perform

exploratory

data analysis to understand the variables and their relationships.

Linear Regression Analysis: Apply linear regression to find a model that best

fits the data. Analyze the relationship between variables and interpret the

significance of the model.

Non$-$Linear Regression Analysis: Implement non$-$linear regression models.

Compare the

performance of linear and non$-$linear models for your dataset.

Model Evaluation: Use appropriate metrics $($like R$-$squared, RMSE$)$ to

evaluate the

performance of your models. Discuss the strengths and weaknesses of each

model.

Report:

Provide a comprehensive report documenting your methodology, code,

analysis, and findings.

Include visual representations $($graphs$,$ plots$)$ to support your analysis.

Discuss real$-$world applications of your findings in mechanical engineering

Submission:

Submit a Python notebook $(.$ ipynb file$)$ containing all code and outputs.

A written report in PDF format.

Resources:

Python libraries such as NumPy, Pandas, Matplotlib, and SciPy