This project implements a Particle Swarm Optimization (PSO) algorithm to determine the most impactful features in a dataset. The objective is to find the optimal subset of features that results in the highest performance of a classifier. The code is written in a Jupyter Notebook file without using any built-in libraries.

To run the code, you will need the following packages installed:

• pandas
• numpy
• scikit-learn

The PSO algorithm is implemented from scratch using the following steps:

1. Initialize a population of particles, each representing a feature subset.
2. Evaluate the performance of each particle's feature subset using a classifier on the training data and testing data.
3. Update the personal best and global best positions for each particle based on the performance evaluation.
4. Update the velocity of each particle using the personal best and global best positions.
5. Repeat steps 2-4 for a predetermined number of iterations or until a stopping criterion is met (e.g. no improvement in performance).
6. Select the best feature subset represented by the global best particle.
7. Evaluate the performance of the classifier using the selected features on the testing data.
8. Compare the results with the classifier using all the features to show the impact of the feature selection.

To run the code, simply open the Particle-_Swarm_Optimization-PSO-_for_Feature_Selection Jupyter Notebook and run the cells in order.

The results of the feature selection will be displayed in the Jupyter Notebook. The performance of the classifier is evaluated using accuracy, precision, recall, and F1 score. The results with and without feature selection are compared to show the impact of the feature selection.

This project demonstrates the implementation of a Particle Swarm Optimization algorithm for feature selection in a dataset. The results show that the optimal subset of features selected by the PSO algorithm results in better performance compared to using all the features. This technique can be applied to a variety of classification problems to improve the performance of the classifier.