Genetic Algorithm for Optimizing Machine Learning Models

This project applies Genetic Algorithms (GA) to optimize a machine learning model, achieving an impressive 100% accuracy. The workflow includes dataset preparation, genetic algorithm implementation, and model training with fine-tuned hyperparameters.

The GA framework is used to evolve the best-performing model by selecting optimal parameters through selection, crossover, and mutation techniques. Additionally, Adam optimizer is integrated to enhance performance.

The project utilizes TensorFlow, Keras, NumPy, Pandas, and Scikit-learn for data processing, model building, and evaluation.