  Adaptive Boosting with Dynamic Weight Adjustment is an enhancement of the
traditional Adaptive boosting commonly known as AdaBoost, a powerful ensemble
learning technique. Adaptive Boosting with Dynamic Weight Adjustment technique
improves the efficiency and accuracy by dynamically updating the weights of the
instances based on prediction error where the weights are updated in proportion
to the error rather than updating weights uniformly as we do in traditional
Adaboost. Adaptive Boosting with Dynamic Weight Adjustment performs better than
Adaptive Boosting as it can handle more complex data relations, allowing our
model to handle imbalances and noise better, leading to more accurate and
balanced predictions. The proposed model provides a more flexible and effective
approach for boosting, particularly in challenging classification tasks.
