Random Forest

The collection explores diverse applications of machine learning algorithms, particularly focusing on the random forest technique across various domains such as healthcare, agriculture, and cybersecurity. Topics include predictive modeling for heart diseases, fraud detection, crop yield estimation, and early diagnosis of conditions like diabetes and asthma. The emphasis is on enhancing accuracy through novel methodologies, addressing challenges such as data balance and feature selection, and showcasing the potential of these algorithms in practical decision-making and analysis.

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