AutoML Pro: Streamlined Data Processing and Precision model optimization for Regression and Classification

Automated Machine Learning (AutoML) has emerged as a powerful solution for overcoming the complexity and expertise dependency in building machine learning (ML) models. This research focuses on AutoML Pro, a streamlined pipeline designed for efficient data processing and precision model optimization for regression and classification tasks. The proposed system integrates automated data preprocessing, feature selection, model training, hyperparameter tuning, and model evaluation, ensuring accuracy and scalability. Leveraging state-of-the-art techniques such as correlation-based feature selection, dropout regularization, and TensorFlow-based neural networks, AutoML Pro enhances both the performance and interpretability of ML models. Experimental results demonstrate its capability to handle diverse datasets with minimal manual intervention while achieving high precision in predictive analytics. Applications in financial forecasting, healthcare diagnostics, and industrial automation validate the versatility and efficacy of AutoML Pro.