Hybrid Machine Learning Models for Intelligent Data Prediction

Abstract

The rapid growth of digital data has created a need for intelligent prediction models capable of processing complex, heterogeneous, and high-dimensional datasets. Conventional machine learning algorithms often provide good predictive performance for specific data characteristics but may struggle when nonlinear relationships, temporal dependencies, noise, and feature interactions occur simultaneously. This study proposes a hybrid machine learning framework that combines complementary learning techniques to improve intelligent data prediction. The proposed approach integrates preprocessing, feature selection, ensemble learning, and predictive modeling to exploit the strengths of multiple algorithms. A hybrid architecture combining Random Forest and Long Short-Term Memory (LSTM) learning is considered for handling both structured feature relationships and sequential dependencies. The framework includes data preprocessing, missing-value treatment, normalization, feature extraction, model training, and performance evaluation. Prediction performance is assessed using accuracy, precision, recall, F1-score, mean absolute error (MAE), and root mean square error (RMSE), depending on the prediction task. The proposed hybrid strategy is designed to improve prediction reliability, reduce model limitations, and provide a flexible framework for intelligent data analytics. The study demonstrates that combining complementary machine learning models can provide a more robust solution than relying on a single predictive algorithm.