In this project, we use the concept of genetic algorithms and random search to choose the best features from the dataset to get the best results with least features.
The dataset used in this project is not specified, and users can input their own dataset as required. You can Use it with any Data you specify here we use it with Breat cancer dataset.
The approach involves the following steps:
- Split the dataset into training and testing datasets.
- Train different classification models using the training dataset.
- Evaluate the performance of each model using the testing dataset.
- Implement a genetic algorithm to select the best features that optimize the classification model's performance.
- Generate a new population by selecting the fittest chromosomes, applying crossover, and mutation.
- Evaluate the new population's fitness and repeat steps 5 and 6 for a specified number of generations.
- Select the best feature subset found during the genetic algorithm's iterations.