Genetic Algorithm for Hyperparameter Tuning

[Cgarg9]

Description:

Use a Genetic Algorithm (GA) to optimize hyperparameters for a LightGBM model.

Tasks:

• Load a classification dataset (e.g., Credit Card Fraud Detection).
• Train a LightGBM model with default parameters.
• Implement GA-based hyperparameter tuning to optimize learning_rate, max_depth, and num_leaves.
• Compare Genetic Algorithm vs Grid Search in terms of performance and runtime.
• Name the notebook genetic_algo_lgbm.ipynb.
• Update the README file with relevant references.