NEURAL NETWORK

I explored various machine learning models including logistic regression, decision tree classifier, support vector classifier, Gaussian Naive Bayes, random forest classifier, AdaBoost classifier, gradient boost classifier, XGBoost classifier, and K-neighbors classifier. After extensive testing and optimization, the Random Forest Classifier emerged as the top performer, achieving a perfect 100% accuracy. To fine-tune the model, I employed Grid Search Cross-Validation, a hyperparameter tuning technique, which further enhanced the performance. The robustness of the random forest classifier, with its low bias and low variance, ensured there were no overfitting concerns.