TowardsDataScience article: https://towardsdatascience.com/feature-selection-with-simulated-annealing-in-python-clearly-explained-1808db14f8fa

Feature selection is a key step in machine learning as it boosts computational efficiency and predictive performance by keeping only the most relevant predictors.

Beyond the popular supervised feature selection classes like filter and wrapper methods, global search methods like simulated annealing are also powerful techniques at our disposal.

In this project, we delve into the theory and application of simulated annealing for feature selection.

• data: Titanic dataset (raw and processed)
• images: Set of images and visualizations used to demonstrate algorithm
• notebooks: Jupyter notebooks for the different steps of the project i.e. data pre-processing, baseline modeling, and running of feature selection with simulated annealing algorithm
• results: CSV files of the output from algorithm runs
• src: Python scripts for simulated annealing algorithm for feature selection
  • main.py: Main script containing algorithm. In CLI, cd into src folder, then execute python main.py
  • utils.py: Utils script containing ML model function (i.e. random forest classifier)

• https://researchgate.net/publication/227061666_Computing_the_Initial_Temperature_of_Simulated_Annealing
• https://search.r-project.org/CRAN/refmans/caret/html/safs.html
• https://iopscience.iop.org/article/10.1088/1742-6596/1752/1/012030/pdf
• https://topepo.github.io/caret/feature-selection-using-simulated-annealing.html
• https://www.slideshare.net/kaalnath/simulated-annealingppt
• https://santhoshhari.github.io/simulated_annealing/
• https://www.feat.engineering/simulated-annealing.html
• https://towardsdatascience.com/optimization-techniques-simulated-annealing-d6a4785a1de7
• https://www.sciencedirect.com/science/article/abs/pii/S0377221704005892
• https://www.youtube.com/watch?v=Dp1irQX-c0Q&ab_channel=Udacity
• https://link.springer.com/article/10.1007/s10878-020-00607-y
• https://arxiv.org/pdf/1906.01504.pdf