• K. Shiva Kumar
• P. Venkat Purna Sai
• P. Kiran
• Dr. S. Sreekanth

Hybrid machine learning techniques offer a powerful solution for addressing complex classification problems by integrating the strengths of different algorithms. This study proposes a unique hybrid model that combines Random Forest, XGBoost, and Long Short-Term Memory (LSTM) networks to exploit their distinct capabilities. Random Forest provides reliable feature selection and ensemble-based predictions, while XGBoost enhances model performance through efficient gradient boosting. LSTM networks are included to capture sequential patterns and dependencies in temporal data, making the framework suitable for both static and dynamic datasets. The hybrid model is tested on a variety of benchmark datasets, demonstrating enhanced accuracy, improved generalization, and reduced overfitting compared to standalone models and conventional hybrid approaches. Additionally, the framework proves to be scalable and adaptable, making it applicable across various domains, such as healthcare, finance, and natural language processing. The results emphasize the value of integrating multiple machine learning algorithms to overcome individual model limitations and achieve high performance in classification tasks.

Hybrid Machine Learning, Random Forest, XGBoost, Long Short-Term Memory (LSTM), Classification Accuracy.

"Implementing Machine Learning Algorithms for Classifying the data- Random Forest, XGBoost, LSTM, Hybrid Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 4, page no.k746-k754, April-2025, Available :http://www.jetir.org/papers/JETIR2504A92.pdf

"Implementing Machine Learning Algorithms for Classifying the data- Random Forest, XGBoost, LSTM, Hybrid Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 4, page no. ppk746-k754, April-2025, Available at : http://www.jetir.org/papers/JETIR2504A92.pdf